diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..2a3585f
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,100 @@
+name: docs
+permissions:
+  contents: write
+  pull-requests: write
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - .pre-commit-config.yaml
+      - .github/workflows/docs.yml
+      - '**.py'
+      - '**.ipynb'
+      - '**.html'
+      - '**.js'
+      - '**.md'
+      - uv.lock
+      - pyproject.toml
+      - mkdocs.yml
+      - '**.png'
+      - '**.svg'
+  pull_request:
+    branches:
+      - main
+    paths:
+      - .pre-commit-config.yaml
+      - .github/workflows/docs.yml
+      - '**.py'
+      - '**.ipynb'
+      - '**.js'
+      - '**.html'
+      - uv.lock
+      - pyproject.toml
+      - '**.md'
+      - mkdocs.yml
+      - '**.png'
+      - '**.svg'
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4.2.2
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182
+        with:
+          version: "0.5.21"
+          enable-cache: true
+
+      - name: Set up Python
+        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
+        with:
+          python-version-file: ".python-version"
+
+      - name: Install the project
+        run: uv sync --all-extras --all-groups
+
+      - name: Build docs
+        run: uv run mkdocs build
+
+      - name: Create .nojekyll file
+        run: touch site/.nojekyll
+
+      - name: Upload artifact
+        uses: actions/upload-artifact@v4
+        with:
+          name: docs-site
+          path: site/
+          retention-days: 1
+
+  deploy:
+    needs: build
+    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4.2.2
+
+      - name: Configure Git Credentials
+        run: |
+          git config user.name github-actions[bot]
+          git config user.email 41898282+github-actions[bot]@users.noreply.github.com
+
+      - name: Download artifact
+        uses: actions/download-artifact@v4
+        with:
+          name: docs-site
+          path: site
+
+      - name: Ensure .nojekyll exists
+        run: touch site/.nojekyll
+
+      - name: Deploy to Github pages
+        uses: JamesIves/github-pages-deploy-action@6c2d9db40f9296374acc17b90404b6e8864128c8
+        with:
+          branch: github_pages
+          folder: site
diff --git a/.github/workflows/docs_build.yml b/.github/workflows/docs_build.yml
deleted file mode 100644
index b882880..0000000
--- a/.github/workflows/docs_build.yml
+++ /dev/null
@@ -1,41 +0,0 @@
-name: docs (build)
-
-on:
-  pull_request:
-    branches:
-      - main
-    paths:
-      - .pre-commit-config.yaml
-      - .github/workflows/docs_build.yml
-      - '**.py'
-      - '**.ipynb'
-      - '**.js'
-      - '**.html'
-      - uv.lock
-      - pyproject.toml
-      - '**.rst'
-      - '**.md'
-
-jobs:
-  build:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4.2.2
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182
-        with:
-          # Install a specific version of uv.
-          version: "0.5.21"
-          enable-cache: true
-
-      - name: "Set up Python"
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
-        with:
-          python-version-file: ".python-version"
-
-      - name: Install the project
-        run: uv sync --all-extras --all-groups
-
-      - name: Build docs
-        run: cd docs && rm -rf source/reference/api/_autosummary && uv run make html
diff --git a/.github/workflows/docs_deploy.yml b/.github/workflows/docs_deploy.yml
deleted file mode 100644
index 4f0ec5c..0000000
--- a/.github/workflows/docs_deploy.yml
+++ /dev/null
@@ -1,56 +0,0 @@
-name: docs
-
-on:
-  push:
-    branches:
-      - main
-    paths:
-      - .pre-commit-config.yaml
-      - .github/workflows/code_checks.yml
-      - .github/workflows/docs_build.yml
-      - .github/workflows/docs_deploy.yml
-      - .github/workflows/integration_tests.yml
-      - '**.py'
-      - '**.ipynb'
-      - '**.html'
-      - '**.js'
-      - uv.lock
-      - pyproject.toml
-      - '**.rst'
-      - '**.md'
-
-jobs:
-  deploy:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4.2.2
-        with:
-          submodules: 'true'
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@0c5e2b8115b80b4c7c5ddf6ffdd634974642d182
-        with:
-          # Install a specific version of uv.
-          version: "0.5.21"
-          enable-cache: true
-
-      - name: "Set up Python"
-        uses: actions/setup-python@8d9ed9ac5c53483de85588cdf95a591a75ab9f55
-        with:
-          python-version-file: ".python-version"
-
-      - name: Install the project
-        run: uv sync --all-extras --all-groups
-
-      - name: Build docs
-        run: |
-          cd docs
-          rm -rf source/reference/api/_autosummary
-          uv run make html
-          touch build/html/.nojekyll
-
-      - name: Deploy to Github pages
-        uses: JamesIves/github-pages-deploy-action@6c2d9db40f9296374acc17b90404b6e8864128c8
-        with:
-          branch: github_pages
-          folder: docs/build/html
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 9414adb..5937b69 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -12,7 +12,11 @@ repos:
       - id: mixed-line-ending
         args: [--fix=lf]
       - id: detect-private-key
+      - id: check-byte-order-marker
+      - id: check-merge-conflict
+      - id: check-symlinks
       - id: check-yaml
+        args: [--unsafe]
       - id: check-toml
 
   - repo: https://github.com/astral-sh/uv-pre-commit
diff --git a/docs/Makefile b/docs/Makefile
deleted file mode 100644
index d0c3cbf..0000000
--- a/docs/Makefile
+++ /dev/null
@@ -1,20 +0,0 @@
-# Minimal makefile for Sphinx documentation
-#
-
-# You can set these variables from the command line, and also
-# from the environment for the first two.
-SPHINXOPTS    ?=
-SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
-
-# Put it first so that "make" without argument is like "make help".
-help:
-	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
-
-.PHONY: help Makefile
-
-# Catch-all target: route all unknown targets to Sphinx using the new
-# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
-%: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/api.md b/docs/api.md
new file mode 100644
index 0000000..4819166
--- /dev/null
+++ b/docs/api.md
@@ -0,0 +1,104 @@
+# API Reference
+
+## Top Level Module
+
+::: mmlearn
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## CLI Module
+
+::: mmlearn.cli
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## Configuration Module
+
+::: mmlearn.conf
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## Datasets Module
+
+::: mmlearn.datasets
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Core Datasets Components
+
+::: mmlearn.datasets.core
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Dataset Processors
+
+::: mmlearn.datasets.processors
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## Modules
+
+::: mmlearn.modules
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Encoders
+
+::: mmlearn.modules.encoders
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Layers
+
+::: mmlearn.modules.layers
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Losses
+
+::: mmlearn.modules.losses
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Learning Rate Schedulers
+
+::: mmlearn.modules.lr_schedulers
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+### Metrics
+
+::: mmlearn.modules.metrics
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## Tasks
+
+::: mmlearn.tasks
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+## Utilities
+
+::: mmlearn.hf_utils
+    options:
+      show_root_heading: true
+      show_root_full_path: true
+
+::: mmlearn.constants
+    options:
+      show_root_heading: true
+      show_root_full_path: true
diff --git a/docs/assets/favicon-48x48.svg b/docs/assets/favicon-48x48.svg
new file mode 100644
index 0000000..3cd92e5
--- /dev/null
+++ b/docs/assets/favicon-48x48.svg
@@ -0,0 +1,9 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="48px" height="48px" viewBox="0 0 48 48" version="1.1">
+<defs>
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+</svg>
diff --git a/docs/contributing.md b/docs/contributing.md
new file mode 100644
index 0000000..c1a5162
--- /dev/null
+++ b/docs/contributing.md
@@ -0,0 +1,117 @@
+# Contributing to mmlearn
+
+Thanks for your interest in contributing to mmlearn!
+
+To submit PRs, please fill out the PR template along with the PR. If the PR fixes an issue, please include a link to the
+PR to the issue. Below are some details around important things to consider before contributing to the library. A table
+of contents also appears below for navigation.
+
+- [Development Practices](#development-practices)
+- [Development Requirements](#development-requirements)
+- [Coding Guidelines, Formatters, and Checks](#coding-guidelines-formatters-and-checks)
+- [Code Documentation](#code-documentation)
+- [Tests](#tests)
+
+## Development Practices
+
+We use the standard git development flow of branch and merge to main with PRs on GitHub. At least one member of the core
+team needs to approve a PR before it can be merged into main. As mentioned above, tests are run automatically on PRs with
+a merge target of main. Furthermore, a suite of static code checkers and formatters are also run on said PRs. These also
+need to pass for a PR to be eligible for merging into the main branch of the library. Currently, such checks run on python3.9.
+
+## Development Requirements
+
+For development and testing, we use [uv](https://github.com/astral-sh/uv?tab=readme-ov-file#installation) for dependency
+management. The library dependencies and those for development and testing are listed in the `pyproject.toml` file.
+You may use whatever virtual environment management tool that you would like.
+These include uv, conda, and virtualenv.
+
+The easiest way to create and activate a virtual environment for development using uv is:
+```bash
+uv sync --dev --all-extras
+```
+
+Note that the with command is installing all libraries required for the full development workflow. See the `pyproject.toml`
+file for additional details as to what is installed with each of these options.
+
+If you need to update the environment libraries, you should change the requirements in the `pyproject.toml` and then update
+the `uv.lock` using the command `uv lock`.
+
+## Coding Guidelines, Formatters, and Checks
+
+For code style, we recommend the [PEP 8 style guide](https://peps.python.org/pep-0008/).
+
+We use [ruff](https://docs.astral.sh/ruff/) for code formatting and static code analysis. Ruff checks various rules including
+[flake8](https://docs.astral.sh/ruff/faq/#how-does-ruff-compare-to-flake8). The pre-commit hooks show errors which you need
+to fix before submitting a PR.
+
+Last but not least, we use type hints in our code which are checked using [mypy](https://mypy.readthedocs.io/en/stable/).
+The mypy checks are strictly enforced. That is, all mypy checks must pass or the associated PR will not be merge-able.
+
+The settings for `mypy` and `ruff` can be found the `pyproject.toml` files and some standard checks are defined directly
+in the `.pre-commit-config.yaml` settings.
+
+All of these checks and formatters are invoked by pre-commit hooks. These hooks are run remotely on GitHub. In order to
+ensure that your code conforms to these standards, and, therefore, passes the remote checks, you can install the pre-commit
+hooks to be run locally. This is done by running (with your environment active)
+
+```bash
+pre-commit install
+```
+
+To run the checks, some of which will automatically re-format your code to fit the standards, you can run
+```bash
+pre-commit run --all-files
+```
+It can also be run on a subset of files by omitting the `--all-files` option and pointing to specific files or folders.
+
+If you're using VS Code for development, pre-commit should setup git hooks that execute the pre-commit checks each time
+you check code into your branch through the integrated source-control as well. This will ensure that each of your commits
+conform to the desired format before they are run remotely and without needing to remember to run the checks before pushing
+to a remote. If this isn't done automatically, you can find instructions for setting up these hooks manually online.
+
+## Code Documentation
+
+For code documentation, we try to adhere to the [numpy format](https://numpydoc.readthedocs.io/en/latest/format.html).
+For development, __any non-trivial or non-obvious methods added to the library should have a doc string__. For our library
+this applies only to code added to the main library in `mmlearn`. Code outside the core library folder, such as tests,
+need not incorporate the strict rules of documentation, though clarifying and helpful comments in that code is also
+__strongly encouraged__.
+
+__NOTE__: As a matter of convention choice, classes are documented at the "class" level rather than through their `__init__`
+functions.
+
+If you are using VS Code a very helpful integration is available to facilitate the creation of properly formatted doc-strings
+called autoDocstring [VS Code Page](https://marketplace.visualstudio.com/items?itemName=njpwerner.autodocstring) and
+[Documentation](https://github.com/NilsJPWerner/autoDocstring). This tool will automatically generate a docstring template
+when starting a docstring with triple quotation marks (`"""`). To get the correct format, the following settings should
+be prescribed in your VS Code settings JSON:
+
+```json
+{
+    "autoDocstring.customTemplatePath": "",
+    "autoDocstring.docstringFormat": "numpy",
+    "autoDocstring.generateDocstringOnEnter": true,
+    "autoDocstring.guessTypes": true,
+    "autoDocstring.includeExtendedSummary": false,
+    "autoDocstring.includeName": false,
+    "autoDocstring.logLevel": "Info",
+    "autoDocstring.quoteStyle": "\"\"\"",
+    "autoDocstring.startOnNewLine": true
+}
+```
+
+## Tests
+
+All tests for the library are housed in the `tests` folder. The unit and integration tests are run using `pytest`. These
+tests are automatically run through GitHub integrations on PRs to the main branch of this repository. PRs that fail any
+of the tests will not be eligible to be merged until they are fixed.
+
+To run all tests in the tests folder one only needs to run (with the venv active)
+```bash
+pytest .
+```
+To run a specific test with pytest, one runs
+```bash
+pytest tests/datasets/test_combined_dataset.py
+```
diff --git a/docs/source/index.rst b/docs/index.md
similarity index 52%
rename from docs/source/index.rst
rename to docs/index.md
index 8e2a187..7d47792 100644
--- a/docs/source/index.rst
+++ b/docs/index.md
@@ -1,17 +1,11 @@
-mmlearn documentation
-=====================
+# mmlearn documentation
 
 *mmlearn* aims at enabling the evaluation of existing multimodal representation learning methods, as well as facilitating
 experimentation and research for new techniques.
 
+## Contents
 
-Contents
---------
-
-.. toctree::
-   :maxdepth: 2
-
-   installation
-   user_guide
-   contributing
-   api
+- [Installation](installation.md)
+- [User Guide](user_guide.md)
+- [Contributing](contributing.md)
+- [API Reference](api.md)
diff --git a/docs/installation.md b/docs/installation.md
new file mode 100644
index 0000000..8dbc1c8
--- /dev/null
+++ b/docs/installation.md
@@ -0,0 +1,54 @@
+# Installation
+
+!!! tip "Prerequisites"
+    For local development, it is generally recommended to install *mmlearn* in a non-global environment (e.g. venv or conda).
+    This will allow you to use different versions of *mmlearn* for different projects.
+
+    You can create a virtual environment using venv with the following command:
+    ```bash
+    python3 -m venv /path/to/new/virtual/environment
+    source /path/to/new/virtual/environment/bin/activate
+    ```
+
+## Installing from PyPI
+
+mmlearn is published on the [Python Package Index](https://pypi.org/project/mmlearn/) and can be installed using pip.
+
+Run the following command to install the library:
+
+```bash
+python3 -m pip install mmlearn
+```
+
+!!! note
+    `mmlearn` has several optional dependencies that are used for specific functionality.
+    For example, the [peft](https://huggingface.co/docs/peft/index) library for parameter-efficient finetuning.
+    Hence, `peft` can be installed using:
+
+    ```bash
+    python3 -m pip install mmlearn[peft]
+    ```
+
+    Specific sets of dependencies are listed below.
+
+    | Dependency | pip extra | Notes |
+    |------------|-----------|-------|
+    | torchvision, timm, opencv-python | vision | Allows use of computer vision models and image processing functionality |
+    | torchaudio | audio | Allows use of audio processing and audio model functionality |
+    | peft | peft | Allows use of parameter-efficient fine-tuning methods |
+
+## Installing from source
+
+You can install mmlearn directly from a clone of the Git repository.
+This can be done either by cloning the repo and installing from the local clone,
+or simply installing directly via git.
+
+```bash
+git clone https://github.com/VectorInstitute/mmlearn.git
+cd mmlearn
+python3 -m pip install -e .
+```
+
+```bash
+pip install git+https://github.com/VectorInstitute/mmlearn.git
+```
diff --git a/docs/make.bat b/docs/make.bat
deleted file mode 100644
index dc1312a..0000000
--- a/docs/make.bat
+++ /dev/null
@@ -1,35 +0,0 @@
-@ECHO OFF
-
-pushd %~dp0
-
-REM Command file for Sphinx documentation
-
-if "%SPHINXBUILD%" == "" (
-	set SPHINXBUILD=sphinx-build
-)
-set SOURCEDIR=source
-set BUILDDIR=build
-
-%SPHINXBUILD% >NUL 2>NUL
-if errorlevel 9009 (
-	echo.
-	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
-	echo.installed, then set the SPHINXBUILD environment variable to point
-	echo.to the full path of the 'sphinx-build' executable. Alternatively you
-	echo.may add the Sphinx directory to PATH.
-	echo.
-	echo.If you don't have Sphinx installed, grab it from
-	echo.https://www.sphinx-doc.org/
-	exit /b 1
-)
-
-if "%1" == "" goto help
-
-%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-goto end
-
-:help
-%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
-
-:end
-popd
diff --git a/docs/overrides/partials/copyright.html b/docs/overrides/partials/copyright.html
new file mode 100644
index 0000000..776166c
--- /dev/null
+++ b/docs/overrides/partials/copyright.html
@@ -0,0 +1,22 @@
+<div class="md-copyright">
+  <div class="md-footer-vector">
+    <a href="https://vectorinstitute.ai" target="_blank" rel="noopener">
+      <img
+        src="{{ config.theme.logo_footer | url }}"
+        alt="Vector Institute Logo"
+        height="24"
+      />
+    </a>
+  </div>
+  {% if config.copyright %}
+  <div class="md-copyright__highlight">{{ config.copyright }}</div>
+  {% endif %} {% if not config.extra.generator == false %} Made with
+  <a
+    href="https://squidfunk.github.io/mkdocs-material/"
+    target="_blank"
+    rel="noopener"
+  >
+    Material for MkDocs
+  </a>
+  {% endif %}
+</div>
diff --git a/docs/overrides/partials/logo.html b/docs/overrides/partials/logo.html
new file mode 100644
index 0000000..2ed1c76
--- /dev/null
+++ b/docs/overrides/partials/logo.html
@@ -0,0 +1,5 @@
+{% if config.theme.logo %}
+<img src="{{ config.theme.logo | url }}" alt="logo">
+{% else %}
+<span class="md-header__title">{{ config.site_name }}</span>
+{% endif %}
diff --git a/docs/source/_static/custom.js b/docs/source/_static/custom.js
deleted file mode 100644
index 738d145..0000000
--- a/docs/source/_static/custom.js
+++ /dev/null
@@ -1,6 +0,0 @@
-requirejs.config({
-  paths: {
-    base: '/static/base',
-    plotly: 'https://cdn.plot.ly/plotly-2.30.0.min.js?noext',
-  },
-});
diff --git a/docs/source/_static/logos/vector_logo.png b/docs/source/_static/logos/vector_logo.png
deleted file mode 100644
index 3d9b106..0000000
Binary files a/docs/source/_static/logos/vector_logo.png and /dev/null differ
diff --git a/docs/source/_static/require.min.js b/docs/source/_static/require.min.js
deleted file mode 100644
index 73dbde8..0000000
--- a/docs/source/_static/require.min.js
+++ /dev/null
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diff --git a/docs/source/_templates/base.html b/docs/source/_templates/base.html
deleted file mode 100644
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-        {%- endif -%}
-        {%- if hasdoc('search') -%}
-          <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
-        {%- endif -%}
-        {%- if hasdoc('copyright') -%}
-          <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
-        {%- endif -%}
-        {%- if next -%}
-          <link rel="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}" />
-        {%- endif -%}
-        {%- if prev -%}
-          <link rel="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}" />
-        {%- endif -%}
-        {#- rel="canonical" (set by html_baseurl) -#}
-        {%- if pageurl %}
-        <link rel="canonical" href="{{ pageurl|e }}" />
-        {%- endif %}
-    {%- endblock linktags %}
-
-    {# Favicon #}
-    {%- if favicon_url -%}
-      <link rel="shortcut icon" href="{{ favicon_url }}"/>
-    {%- endif -%}
-
-    {#- Generator banner -#}
-    <meta name="generator" content="sphinx-{{ sphinx_version }}, furo {{ furo_version }}"/>
-
-    {# Bing webmasters meta tag #}
-    <meta name="msvalidate.01" content="8D2A5032F006424CAF54C0DDCD16F666" />
-
-    {%- endblock site_meta -%}
-
-    {#- Site title -#}
-    {%- block htmltitle -%}
-      {# See Sphinx monkey patch in conf.py #}
-      {% if 'title' in metas %}
-        <title>{{ metas.title }}</title>
-      {% elif not docstitle %}
-        <title>{{ title|striptags|e }}</title>
-      {% elif pagename == master_doc %}
-        <title>{{ docstitle|striptags|e }}</title>
-      {% else %}
-        <title>{{ title|striptags|e }} - {{ docstitle|striptags|e }}</title>
-      {% endif %}
-    {%- endblock -%}
-
-    {%- block styles -%}
-
-    {# Custom stylesheets #}
-    {%- block regular_styles -%}
-    {%- for css in css_files -%}
-      {% if css|attr("filename") -%}
-        {{ css_tag(css) }}
-      {%- else -%}
-        <link rel="stylesheet" href="{{ pathto(css, 1)|e }}" type="text/css" />
-      {%- endif %}
-    {% endfor -%}
-    {%- endblock regular_styles -%}
-
-    {#- Theme-related stylesheets -#}
-    {%- block theme_styles %}
-    {% include "partials/_head_css_variables.html" with context %}
-    {%- endblock -%}
-
-    {%- block extra_styles %}
-    {%- endblock -%}
-
-    {%- endblock styles -%}
-
-    {#- Custom front matter #}
-    {%- block extrahead -%}{%- endblock -%}
-
-    {# Custom JS #}
-    {%- block regular_scripts -%}
-    {% for path in script_files -%}
-      {{ js_tag(path) }}
-    {% endfor -%}
-    {%- endblock regular_scripts -%}
-
-    {# Theme-related JavaScript code #}
-    {%- block theme_scripts -%}
-    {%- endblock -%}
-
-    {# Footer icons #}
-    <script src="https://kit.fontawesome.com/2c1f516901.js" crossorigin="anonymous"></script>
-
-  </head>
-  <body>
-    {% block body %}
-    <script>
-      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
-    </script>
-    {% endblock %}
-
-    {%- block scripts -%}
-    {%- endblock scripts -%}
-  </body>
-</html>
diff --git a/docs/source/_templates/custom-class-template.rst b/docs/source/_templates/custom-class-template.rst
deleted file mode 100644
index 43864c8..0000000
--- a/docs/source/_templates/custom-class-template.rst
+++ /dev/null
@@ -1,29 +0,0 @@
-{{ fullname | escape | underline}}
-
-.. currentmodule:: {{ module }}
-
-.. autoclass:: {{ objname }}
-   :members:
-   :show-inheritance:
-   :inherited-members:
-   :special-members: __call__, __getitem__, __iter__
-
-   {% block methods %}
-   {% if methods %}
-   .. rubric:: {{ _('Methods') }}
-
-   {% for item in methods %}
-   .. automethod:: ~{{ name }}.{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-   {% block attributes %}
-   {% if attributes %}
-   .. rubric:: {{ _('Attributes') }}
-
-   {% for item in attributes %}
-   .. autoattribute:: ~{{ name }}.{{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
diff --git a/docs/source/_templates/custom-module-template.rst b/docs/source/_templates/custom-module-template.rst
deleted file mode 100644
index 1db765a..0000000
--- a/docs/source/_templates/custom-module-template.rst
+++ /dev/null
@@ -1,60 +0,0 @@
-{{ fullname | escape | underline}}
-
-.. automodule:: {{ fullname }}
-
-   {% block attributes %}
-   {% if attributes %}
-   .. rubric:: Module attributes
-
-   {% for item in attributes %}
-   .. autodata:: {{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-   {% block functions %}
-   {% if functions %}
-   .. rubric:: {{ _('Functions') }}
-
-   {% for item in functions %}
-   .. autofunction:: {{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-   {% block classes %}
-   {% if classes %}
-   .. rubric:: {{ _('Classes') }}
-
-   .. autosummary::
-      :toctree:
-         :hidden:
-      :template: custom-class-template.rst
-      :nosignatures:
-   {% for item in classes %}
-      {{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-   {% block exceptions %}
-   {% if exceptions %}
-   .. rubric:: {{ _('Exceptions') }}
-
-   {% for item in exceptions %}
-   .. autoexception:: {{ item }}
-   {%- endfor %}
-   {% endif %}
-   {% endblock %}
-
-{% block modules %}
-{% if modules %}
-.. autosummary::
-   :toctree:
-   :template: custom-module-template.rst
-   :recursive:
-{% for item in modules %}
-   {{ item }}
-{%- endfor %}
-{% endif %}
-{% endblock %}
diff --git a/docs/source/_templates/page.html b/docs/source/_templates/page.html
deleted file mode 100644
index 81b2131..0000000
--- a/docs/source/_templates/page.html
+++ /dev/null
@@ -1,219 +0,0 @@
-{% extends "base.html" %}
-
-{% block body -%}
-{{ super() }}
-{% include "partials/icons.html" %}
-
-<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
-<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
-<label class="overlay sidebar-overlay" for="__navigation">
-  <div class="visually-hidden">Hide navigation sidebar</div>
-</label>
-<label class="overlay toc-overlay" for="__toc">
-  <div class="visually-hidden">Hide table of contents sidebar</div>
-</label>
-
-{% if theme_announcement -%}
-<div class="announcement">
-  <aside class="announcement-content">
-    {% block announcement %} {{ theme_announcement }} {% endblock announcement %}
-  </aside>
-</div>
-{%- endif %}
-
-<div class="page">
-  <header class="mobile-header">
-    <div class="header-left">
-      <label class="nav-overlay-icon" for="__navigation">
-        <div class="visually-hidden">Toggle site navigation sidebar</div>
-        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
-      </label>
-    </div>
-    <div class="header-center">
-      <a href="{{ pathto(master_doc) }}"><div class="brand">{{ docstitle if docstitle else project }}</div></a>
-    </div>
-    <div class="header-right">
-      <div class="theme-toggle-container theme-toggle-header">
-        <button class="theme-toggle">
-          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-        </button>
-      </div>
-      <label class="toc-overlay-icon toc-header-icon{% if furo_hide_toc %} no-toc{% endif %}" for="__toc">
-        <div class="visually-hidden">Toggle table of contents sidebar</div>
-        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-      </label>
-    </div>
-  </header>
-  <aside class="sidebar-drawer">
-    <div class="sidebar-container">
-      {% block left_sidebar %}
-      <div class="sidebar-sticky">
-        {%- for sidebar_section in sidebars %}
-          {%- include sidebar_section %}
-        {%- endfor %}
-      </div>
-      {% endblock left_sidebar %}
-    </div>
-  </aside>
-  <div class="main">
-    <div class="content">
-      <div class="article-container">
-        <a href="#" class="back-to-top muted-link">
-          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
-            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
-          </svg>
-          <span>{% trans %}Back to top{% endtrans %}</span>
-        </a>
-        <div class="content-icon-container">
-          {% if theme_top_of_page_button == "edit" -%}
-          {%- include "components/edit-this-page.html" with context -%}
-          {%- elif theme_top_of_page_button != None -%}
-          {{ warning("Got an unsupported value for 'top_of_page_button'") }}
-          {%- endif -%}
-          {#- Theme toggle -#}
-          <div class="theme-toggle-container theme-toggle-content">
-            <button class="theme-toggle">
-              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
-              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
-              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
-              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
-            </button>
-          </div>
-          <label class="toc-overlay-icon toc-content-icon{% if furo_hide_toc %} no-toc{% endif %}" for="__toc">
-            <div class="visually-hidden">Toggle table of contents sidebar</div>
-            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
-          </label>
-        </div>
-        <article role="main">
-          {% block content %}{{ body }}{% endblock %}
-        </article>
-      </div>
-      <footer>
-        {% block footer %}
-        <div class="related-pages">
-          {% if next -%}
-            <a class="next-page" href="{{ next.link }}">
-              <div class="page-info">
-                <div class="context">
-                  <span>{{ _("Next") }}</span>
-                </div>
-                <div class="title">{{ next.title }}</div>
-              </div>
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-            </a>
-          {%- endif %}
-          {% if prev -%}
-            <a class="prev-page" href="{{ prev.link }}">
-              <svg class="furo-related-icon"><use href="#svg-arrow-right"></use></svg>
-              <div class="page-info">
-                <div class="context">
-                  <span>{{ _("Previous") }}</span>
-                </div>
-                {% if prev.link == pathto(master_doc) %}
-                <div class="title">{{ _("Home") }}</div>
-                {% else %}
-                <div class="title">{{ prev.title }}</div>
-                {% endif %}
-              </div>
-            </a>
-          {%- endif %}
-        </div>
-        <div class="bottom-of-page">
-          <div class="left-details">
-            {%- if show_copyright %}
-            <div class="copyright">
-              {%- if hasdoc('copyright') %}
-                {% trans path=pathto('copyright'), copyright=copyright|e -%}
-                  <a href="{{ path }}">Copyright</a> &#169; {{ copyright }}
-                {%- endtrans %}
-              {%- else %}
-                {% trans copyright=copyright|e -%}
-                  Copyright &#169; {{ copyright }}
-                {%- endtrans %}
-              {%- endif %}
-            </div>
-            {%- endif %}
-            <!--
-            {% trans %}Made with {% endtrans -%}
-            {%- if show_sphinx -%}
-            {% trans %}<a href="https://www.sphinx-doc.org/">Sphinx</a> and {% endtrans -%}
-            <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
-            {% endif -%}
-            {% trans %}
-            <a href="https://github.com/pradyunsg/furo">Furo</a>
-            {% endtrans %}
-            -->
-            {%- if last_updated -%}
-            <div class="last-updated">
-              {% trans last_updated=last_updated|e -%}
-                Last updated on {{ last_updated }}
-              {%- endtrans -%}
-            </div>
-            {%- endif %}
-          </div>
-          <div class="right-details">
-            {% if theme_footer_icons or READTHEDOCS -%}
-            <div class="icons">
-              {% if theme_footer_icons -%}
-              {% for icon_dict in theme_footer_icons -%}
-              <a class="muted-link {{ icon_dict.class }}" href="{{ icon_dict.url }}" aria-label="{{ icon_dict.name }}">
-                {{- icon_dict.html -}}
-              </a>
-              {% endfor %}
-              {%- else -%}
-              {#- Show Read the Docs project -#}
-              {%- if READTHEDOCS and slug -%}
-              <a class="muted-link" href="https://readthedocs.org/projects/{{ slug }}" aria-label="On Read the Docs">
-                <svg x="0px" y="0px" viewBox="-125 217 360 360" xml:space="preserve">
-                  <path fill="currentColor" d="M39.2,391.3c-4.2,0.6-7.1,4.4-6.5,8.5c0.4,3,2.6,5.5,5.5,6.3 c0,0,18.5,6.1,50,8.7c25.3,2.1,54-1.8,54-1.8c4.2-0.1,7.5-3.6,7.4-7.8c-0.1-4.2-3.6-7.5-7.8-7.4c-0.5,0-1,0.1-1.5,0.2 c0,0-28.1,3.5-50.9,1.6c-30.1-2.4-46.5-7.9-46.5-7.9C41.7,391.3,40.4,391.1,39.2,391.3z M39.2,353.6c-4.2,0.6-7.1,4.4-6.5,8.5 c0.4,3,2.6,5.5,5.5,6.3c0,0,18.5,6.1,50,8.7c25.3,2.1,54-1.8,54-1.8c4.2-0.1,7.5-3.6,7.4-7.8c-0.1-4.2-3.6-7.5-7.8-7.4 c-0.5,0-1,0.1-1.5,0.2c0,0-28.1,3.5-50.9,1.6c-30.1-2.4-46.5-7.9-46.5-7.9C41.7,353.6,40.4,353.4,39.2,353.6z M39.2,315.9 c-4.2,0.6-7.1,4.4-6.5,8.5c0.4,3,2.6,5.5,5.5,6.3c0,0,18.5,6.1,50,8.7c25.3,2.1,54-1.8,54-1.8c4.2-0.1,7.5-3.6,7.4-7.8 c-0.1-4.2-3.6-7.5-7.8-7.4c-0.5,0-1,0.1-1.5,0.2c0,0-28.1,3.5-50.9,1.6c-30.1-2.4-46.5-7.9-46.5-7.9 C41.7,315.9,40.4,315.8,39.2,315.9z M39.2,278.3c-4.2,0.6-7.1,4.4-6.5,8.5c0.4,3,2.6,5.5,5.5,6.3c0,0,18.5,6.1,50,8.7 c25.3,2.1,54-1.8,54-1.8c4.2-0.1,7.5-3.6,7.4-7.8c-0.1-4.2-3.6-7.5-7.8-7.4c-0.5,0-1,0.1-1.5,0.2c0,0-28.1,3.5-50.9,1.6 c-30.1-2.4-46.5-7.9-46.5-7.9C41.7,278.2,40.4,278.1,39.2,278.3z M-13.6,238.5c-39.6,0.3-54.3,12.5-54.3,12.5v295.7 c0,0,14.4-12.4,60.8-10.5s55.9,18.2,112.9,19.3s71.3-8.8,71.3-8.8l0.8-301.4c0,0-25.6,7.3-75.6,7.7c-49.9,0.4-61.9-12.7-107.7-14.2 C-8.2,238.6-10.9,238.5-13.6,238.5z M19.5,257.8c0,0,24,7.9,68.3,10.1c37.5,1.9,75-3.7,75-3.7v267.9c0,0-19,10-66.5,6.6 C59.5,536.1,19,522.1,19,522.1L19.5,257.8z M-3.6,264.8c4.2,0,7.7,3.4,7.7,7.7c0,4.2-3.4,7.7-7.7,7.7c0,0-12.4,0.1-20,0.8 c-12.7,1.3-21.4,5.9-21.4,5.9c-3.7,2-8.4,0.5-10.3-3.2c-2-3.7-0.5-8.4,3.2-10.3c0,0,0,0,0,0c0,0,11.3-6,27-7.5 C-16,264.9-3.6,264.8-3.6,264.8z M-11,302.6c4.2-0.1,7.4,0,7.4,0c4.2,0.5,7.2,4.3,6.7,8.5c-0.4,3.5-3.2,6.3-6.7,6.7 c0,0-12.4,0.1-20,0.8c-12.7,1.3-21.4,5.9-21.4,5.9c-3.7,2-8.4,0.5-10.3-3.2c-2-3.7-0.5-8.4,3.2-10.3c0,0,11.3-6,27-7.5 C-20.5,302.9-15.2,302.7-11,302.6z M-3.6,340.2c4.2,0,7.7,3.4,7.7,7.7s-3.4,7.7-7.7,7.7c0,0-12.4-0.1-20,0.7 c-12.7,1.3-21.4,5.9-21.4,5.9c-3.7,2-8.4,0.5-10.3-3.2c-2-3.7-0.5-8.4,3.2-10.3c0,0,11.3-6,27-7.5C-16,340.1-3.6,340.2-3.6,340.2z" />
-                </svg>
-              </a>
-              {%- endif -%}
-              {#- Show GitHub repository home -#}
-              {%- if READTHEDOCS and display_github and github_user != "None" and github_repo != "None" -%}
-              <a class="muted-link" href="https://github.com/{{ github_user }}/{{ github_repo }}" aria-label="On GitHub">
-                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
-                  <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
-                </svg>
-              </a>
-              {%- endif -%}
-              {%- endif %}
-            </div>
-            {%- endif %}
-          </div>
-        </div>
-        <div class="footer-container" style="text-align: center;">
-          <div class="logo">
-            <a class="logo" href="https://vectorinstitute.ai/">
-              <span class="logo"></span>
-              <img alt="Logo of the Vector Institute" style="width:90px; max-width:90px;" src="{{ pathto('_static/logos/vector_logo.png', 1) }}" />
-            </a>
-          </div>
-        </div>
-        {% endblock footer %}
-      </footer>
-    </div>
-    <aside class="toc-drawer{% if furo_hide_toc %} no-toc{% endif %}">
-      {% block right_sidebar %}
-      {% if not furo_hide_toc %}
-      <div class="toc-sticky toc-scroll">
-        <div class="toc-title-container">
-          <span class="toc-title">
-            {{ _("On this page") }}
-          </span>
-        </div>
-        <div class="toc-tree-container">
-          <div class="toc-tree">
-            {{ toc }}
-          </div>
-        </div>
-      </div>
-      {% endif %}
-      {% endblock right_sidebar %}
-    </aside>
-  </div>
-</div>
-{%- endblock %}
diff --git a/docs/source/api.rst b/docs/source/api.rst
deleted file mode 100644
index c641f9a..0000000
--- a/docs/source/api.rst
+++ /dev/null
@@ -1,13 +0,0 @@
-API Reference
-=============
-
-.. autosummary::
-   :toctree: reference/api
-   :template: custom-module-template.rst
-   :recursive:
-
-   mmlearn.conf
-   mmlearn.datasets
-   mmlearn.hf_utils
-   mmlearn.modules
-   mmlearn.tasks
diff --git a/docs/source/conf.py b/docs/source/conf.py
deleted file mode 100644
index 3228a71..0000000
--- a/docs/source/conf.py
+++ /dev/null
@@ -1,102 +0,0 @@
-"""Configuration file for the Sphinx documentation builder."""
-
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-import os
-import sys
-
-
-sys.path.insert(0, os.path.abspath("../.."))
-sys.path.insert(0, os.path.abspath("./_ext"))
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-project = "mmlearn"
-copyright = "2024, Vector AI Engineering"  # noqa: A001
-author = "Vector AI Engineering"
-release = "0.1.0b2"
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-extensions = [
-    "sphinx.ext.autodoc",
-    "sphinx.ext.autosectionlabel",
-    "sphinx.ext.autosummary",
-    "sphinx.ext.intersphinx",
-    "sphinx.ext.napoleon",
-    "sphinx.ext.viewcode",
-    "sphinx_autodoc_typehints",
-    "sphinx_copybutton",
-    "sphinx_design",
-    "sphinxcontrib.apidoc",
-    "myst_parser",
-]
-add_module_names = False
-apidoc_module_dir = "../../mmlearn"
-apidoc_output_dir = "reference/api"
-apidoc_excluded_paths = ["tests"]
-apidoc_separate_modules = True
-apidoc_module_first = True
-autoclass_content = "class"
-autodoc_default_options = {
-    "members": True,
-    "undoc-members": False,
-    "private-members": False,
-    "special-members": "__call__,__getitem__,__iter__,__len__",
-    "inherited-members": False,
-    "ignore-module-all": True,
-}
-autodoc_inherit_docstrings = True
-autosectionlabel_prefix_document = True
-autosummary_generate = True
-copybutton_prompt_text = r">>> |\.\.\. "
-copybutton_prompt_is_regexp = True
-intersphinx_mapping = {
-    "python": ("https://docs.python.org/3.10/", None),
-    "numpy": ("http://docs.scipy.org/doc/numpy/", None),
-    "pandas": ("https://pandas.pydata.org/docs/", None),
-    "torch": ("https://pytorch.org/docs/stable/", None),
-    "torchvision": ("https://pytorch.org/vision/stable/", None),
-    "torchaudio": ("https://pytorch.org/audio/stable/", None),
-    "hydra-zen": ("https://mit-ll-responsible-ai.github.io/hydra-zen/", None),
-    "pillow": ("https://pillow.readthedocs.io/en/stable/", None),
-    "lightning": ("https://lightning.ai/docs/pytorch/stable/", None),
-    "torchmetrics": ("https://lightning.ai/docs/torchmetrics/stable/", None),
-    "Pillow": ("https://pillow.readthedocs.io/en/latest/", None),
-    "transformers": ("https://huggingface.co/docs/transformers/en/", None),
-}
-napoleon_google_docstring = False
-napoleon_numpy_docstring = True
-napoleon_include_init_with_doc = True
-napoleon_attr_annotations = True
-set_type_checking_flag = True
-templates_path = ["_templates"]
-
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-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
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-html_theme = "furo"
-html_static_path = ["_static"]
-html_js_files = ["require.min.js", "custom.js"]
-html_additional_pages = {"page": "page.html"}
-html_theme_options = {
-    "dark_css_variables": {
-        "color-brand-primary": "#faad1a",
-        "color-brand-content": "#eb088a",
-        "color-foreground-secondary": "#52c7de",
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diff --git a/docs/source/contributing.rst b/docs/source/contributing.rst
deleted file mode 100644
index 7a661a3..0000000
--- a/docs/source/contributing.rst
+++ /dev/null
@@ -1,2 +0,0 @@
-.. include:: ../../CONTRIBUTING.md
-   :parser: myst_parser.sphinx_
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
deleted file mode 100644
index c4c9652..0000000
--- a/docs/source/installation.rst
+++ /dev/null
@@ -1,67 +0,0 @@
-Installation
-============
-
-.. :toctree::
-
-
-.. tip:: **Prerequisites**
-
-   For local development, it is generally recommended to install *mmlearn* in a non-global environment (e.g. venv or conda)
-   This will allow you to use different versions of *mmlearn* for different projects.
-
-   You can create a virtual environment using venv with the following command:
-
-   .. code:: bash
-
-      python3 -m venv /path/to/new/virtual/environment
-      source /path/to/new/virtual/environment/bin/activate
-
-
-Installing from PyPI
----------------------
-
-mmlearn is published on the `Python Package Index <https://pypi.org/project/mmlearn/>`__ and can be installed using pip.
-
-Run the following command to install the library:
-
-.. code:: bash
-
-   python3 -m pip install mmlearn
-
-.. note::
-   ``mmlearn`` has several optional dependencies that are used for specific functionality.
-   For example, the `peft <https://huggingface.co/docs/peft/index>`__ library for parameter-efficient finetuning.
-   Hence, `peft` can be installed using:
-
-   .. code:: bash
-
-      python3 -m pip install mmlearn[peft]
-
-   Specific sets of dependencies are listed below.
-
-   +-----------------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+
-   | Dependency                        | pip extra                | Notes                                                                                                         |
-   +===================================+==========================+===============================================================================================================+
-   | torchvision, timm, opencv-python  | vision                   | Allows use of computer vision models and image processing functionality                                       |
-   +-----------------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+
-   | torchaudio                        | audio                    | Allows use of audio processing and audio model functionality                                                  |
-   +-----------------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+
-   | peft                              | peft                     | Allows use of parameter-efficient fine-tuning methods                                                         |
-   +-----------------------------------+--------------------------+---------------------------------------------------------------------------------------------------------------+
-
-Installing from source
-----------------------
-
-You can install mmlearn directly from a clone of the Git repository.
-This can be done either by cloning the repo and installing from the local clone,
-on simply installing directly via git.
-
-.. code:: bash
-
-   git clone https://github.com/VectorInstitute/mmlearn.git
-   cd mmlearn
-   python3 -m pip install -e .
-
-.. code:: bash
-
-   pip install git+https://github.com/VectorInstitute/mmlearn.git
diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst
deleted file mode 100644
index 7b911ed..0000000
--- a/docs/source/user_guide.rst
+++ /dev/null
@@ -1,587 +0,0 @@
-User Guide
-==========
-*mmlearn* contains a collection of tools and utilities to help researchers and practitioners easily set up and run training
-or evaluation experiments for multimodal representation learning methods. The toolkit is designed to be modular and extensible.
-We aim to provide a high degree of flexibility in using existing methods, while also allowing users to easily add support
-for new modalities of data, datasets, models and pretraining or evaluation methods.
-
-Much of the power and flexibility of *mmlearn* comes from building on top of the `PyTorch Lightning <https://lightning.ai/docs/pytorch/stable/>`_
-framework and using `Hydra <https://hydra.cc/docs/intro/>`_ and `hydra-zen <https://mit-ll-responsible-ai.github.io/hydra-zen/>`_
-for configuration management. Together, these tools make it easy to define and run experiments with different configurations,
-and to scale up experiments to run on a SLURM cluster.
-
-The goal of this guide is to give you a brief overview of what *mmlearn* is and how you can get started using it.
-
-.. note::
-   *mmlearn* currently only supports training and evaluation of encoder-only models.
-
-   For more detailed information on the features and capabilities of *mmlearn*, please refer to the :doc:`API Reference <api>`.
-
-
-Defining a Dataset
-------------------
-Datasets in *mmlearn* can be defined using PyTorch's :class:`~torch.utils.data.Dataset` or :class:`~torch.utils.data.IterableDataset`
-classes. However, there are two additional requirements for datasets in *mmlearn*:
-
-1. The dataset must return an instance of :class:`~mmlearn.datasets.core.example.Example` from the :meth:`~torch.utils.data.Dataset.__getitem__`
-   method or the :meth:`~torch.utils.data.IterableDataset.__iter__` method.
-2. The :class:`~mmlearn.datasets.core.example.Example` object returned by the dataset must contain the key ``'example_index'``
-   and use modality-specific keys from the :class:`Modalities <mmlearn.datasets.core.modalities.ModalityRegistry>` registry
-   to store the data.
-
-**Example 1**: Defining a map-style dataset in *mmlearn*:
-
-.. code-block:: python
-
-   from torch.utils.data.dataset import Dataset
-
-   from mmlearn.datasets.core import Example, Modalities
-   from mmlearn.constants import EXAMPLE_INDEX_KEY
-
-
-   class MyMapStyleDataset(Dataset[Example]):
-      ...
-      def __getitem__(self, idx: int) -> Example:
-         ...
-         return Example(
-            {
-               EXAMPLE_INDEX_KEY: idx,
-               Modalities.TEXT.name: ...,
-               Modalities.RGB.name: ...,
-               Modalities.RGB.target: ...,
-               Modalities.TEXT.mask: ...,
-               ...
-            }
-         )
-
-**Example 2**: Defining an iterable-style dataset in *mmlearn*:
-
-.. code-block:: python
-
-   from torch.utils.data.dataset import IterableDataset
-
-   from mmlearn.datasets.core import Example, Modalities
-   from mmlearn.constants import EXAMPLE_INDEX_KEY
-
-
-   class MyIterableStyleDataset(IterableDataset[Example]):
-      ...
-      def __iter__(self) -> Generator[Example, None, None]:
-         ...
-         idx = 0
-         for item in items:
-            yield Example(
-               {
-                  EXAMPLE_INDEX_KEY: idx,
-                  Modalities.TEXT.name: ...,
-                  Modalities.AUDIO.name: ...,
-                  Modalities.TEXT.mask: ...,
-                  Modalities.AUDIO.mask: ...,
-                  ...
-               }
-            )
-            idx += 1
-
-The :class:`~mmlearn.datasets.core.example.Example` class represents a single example in the dataset and all the attributes
-associated with it. The class is an extension of the :class:`~collections.OrderedDict` class that provides attribute-style access
-to the dictionary values and handles the creation of the ``'example_ids'`` tuple, combining the ``'example_index'`` and
-``'dataset_index'`` values. The ``'example_index'`` key is created by the dataset object for each example returned by the
-dataset. On the other hand, the ``'dataset_index'`` key is created by the :class:`~mmlearn.datasets.core.combined_dataset.CombinedDataset`
-each :class:`~mmlearn.datasets.core.example.Example` object returned by the dataset.
-
-.. note::
-   All dataset objects in *mmlearn* are wrapped in the :class:`~mmlearn.datasets.core.combined_dataset.CombinedDataset` class,
-   which is a subclass of :class:`torch.utils.data.Dataset`. As such, the user almost never has to add/define the ``'dataset_index'``
-   key explicitly.
-
-   Since batching typically combines data from the same modality into one tensor, both the ``'example_index'`` and ``'dataset_index'``
-   keys are essential for uniquely identifying paired examples across different modalities from the same dataset. The
-   :func:`~mmlearn.datasets.core.example.find_matching_indices` function does exactly this by finding the indices of the
-   examples in a batch that have the same ``'example_ids'`` tuple.
-
-:py:data:`~mmlearn.datasets.core.modalities.Modalities` is an instance of :class:`~mmlearn.datasets.core.modalities.ModalityRegistry`
-singleton class that serves as a global registry for all the modalities supported by *mmlearn*. It allows dot-style access
-registered modalities and their properties. For example, the ``'RGB'`` modality can be accessed using  :py:data:`Modalities.RGB`
-(returns string ``'rgb'``) and the ``'target'`` property of the ``'RGB'`` modality can be accessed using :py:data:`Modalities.RGB.target`
-(returns the string ``'rgb_target'``). It also provides a method to register new modalities and their properties. For example,
-the following code snippet shows how to register a new ``'DNA'`` modality:
-
-.. code-block:: python
-
-   from mmlearn.datasets.core import Modalities
-
-   Modalities.register_modality("dna")
-
-
-Adding New Modules
-------------------
-Modules are building blocks for models and tasks in *mmlearn*. They can be anything from encoders, layers, losses, optimizers,
-learning rate schedulers, metrics, etc. Modules in *mmlearn* are generally defined by extending PyTorch's :class:`nn.Module <torch.nn.Module>`
-class.
-
-Users have the flexibility to design new modules according to their requirements, with the exception of encoder modules
-and modules associated with specific pre-defined tasks (e.g., loss functions for the :class:`~mmlearn.tasks.contrastive_pretraining.ContrastivePretraining` task).
-The forward method of encoder modules must accept a dictionary as input, where the keys are the names of the modalities
-and the values are the corresponding (batched) tensors/data. This format makes it easier to reuse the encoder with different
-modalities and different tasks. In addition, the forward method must return a list-like object where the first element is
-the last layer's output. The following code snippet shows how to define a new text encoder module:
-
-.. code-block:: python
-
-   import torch
-   from torch import nn
-
-   from mmlearn.datasets.core import Modalities
-
-
-   class MyTextEncoder(nn.Module):
-      def __init__(self, input_dim: int, output_dim: int):
-         super().__init__()
-         self.encoder = ...
-
-      def forward(self, inputs: dict[str, torch.Tensor]) -> tuple[torch.Tensor]:
-         out = self.encoder(
-            inputs[Modalities.TEXT.name],
-            inputs.get(
-               "attention_mask", inputs.get(Modalities.TEXT.attention_mask, None)
-            ),
-         )
-         return (out,)
-
-For modules associated with pre-defined tasks, the new modules must adhere to the same function signature as the existing
-modules for that task. For instance, the forward method of a new loss function for the :class:`~mmlearn.tasks.contrastive_pretraining.ContrastivePretraining`
-task must have the following signature to be compatible with the existing loss functions for the task:
-
-.. code-block:: python
-
-   import torch
-
-   from mmlearn.tasks.contrastive_pretraining import LossPairSpec
-
-   def my_contrastive_loss(
-      embeddings: dict[str, torch.Tensor],
-      example_ids: dict[str, torch.Tensor],
-      logit_scale: torch.Tensor,
-      modality_loss_pairs: list[LossPairSpec],
-   ) -> torch.Tensor:
-      ...
-
-
-Adding New Tasks
-----------------
-Tasks in *mmlearn* represent the different training and/or evaluation objectives that can be performed on the data using
-the different modules. Tasks that require training should extend the :class:`~mmlearn.tasks.base.TrainingTask` class, while tasks
-involving only evaluation should extend the :class:`~mmlearn.tasks.hooks.EvaluationHooks` class.
-
-Training Tasks
-~~~~~~~~~~~~~~
-The :class:`~mmlearn.tasks.base.TrainingTask` class is an extension of the :class:`~lightning.pytorch.core.LightningModule`
-class, which itself is an extension of the :class:`~torch.nn.Module` class. The class provides a common interface for training
-tasks in *mmlearn*. It allows users to define the training loop, validation loop, test loop, and the setup for the model,
-optimizer, learning rate scheduler and loss function, all in one place (a functionality inherited from PyTorch Lightning).
-The class also provides hooks for customizing the training loop, validation loop, and test loop, as well as a suite of
-other functionalities like logging, checkpointing and handling distributed training.
-
-.. seealso::
-   For more information on the features and capabilities of the :class:`~mmlearn.tasks.base.TrainingTask` class inherited
-   from PyTorch Lightning, please refer to the PyTorch Lightning `documentation <https://lightning.ai/docs/pytorch/stable/>`_.
-
-To be used with the PyTorch Lightning Trainer, extensions of the :class:`~mmlearn.tasks.base.TrainingTask` class must define
-a `training_step` method. The following code snippet shows the minimum requirements for defining a new task in *mmlearn*:
-
-.. code-block:: python
-
-   from typing import Any, Optional, Union
-   from functools import partial
-
-   import torch
-
-   from mmlearn.tasks.base import TrainingTask
-
-   class MyTask(TrainingTask):
-      def __init__(
-         self,
-         optimizer: Optional[partial[torch.optim.Optimizer]],
-         loss_fn: Optional[torch.nn.Module],
-         lr_scheduler: Optional[
-            Union[
-               dict[str, Union[partial[torch.optim.lr_scheduler.LRScheduler], Any]],
-               partial[torch.optim.lr_scheduler.LRScheduler],
-            ]
-         ] = None,
-      ) -> None:
-         super().__init__(optimizer=optimizer, loss_fn=loss_fn, lr_scheduler=lr_scheduler)
-
-         # Since this class also inherits from torch.nn.Module, we can define the
-         # model and its components directly in the constructor and also define
-         # a forward method for the model as an instance method of this class.
-         # Alternatively, we can pass the model as an argument to the constructor
-         # and assign it to an instance variable.
-         self.model = ...
-
-      def training_step(self, batch: dict[str, Any], batch_idx: int) -> torch.Tensor:
-         outputs = self.model(batch) # or self(batch) if a forward method is defined in this class
-
-         # maybe process outputs here
-
-         loss = self.loss_fn(outputs, ...)
-         return loss
-
-Evaluation Tasks
-~~~~~~~~~~~~~~~~
-The :class:`~mmlearn.tasks.hooks.EvaluationHooks` class is intended to be used for evaluation tasks that don't require training,
-e.g. zero-shot evaluation tasks (as opposed to evaluation tasks like linear probing, which require training). The class provides
-an interface for defining and customizing the evaluation loop.
-
-Classes that inherit from :class:`~mmlearn.tasks.hooks.EvaluationHooks` cannot be run/used on their own. They must be used
-in conjunction with a training task, which will call the hooks defined in the evaluation task during the evaluation phase.
-This way, multiple evaluation tasks can be defined and used with the same training task. The model to be evaluated is
-provided by the training task to the evaluation task.
-
-Training tasks that wish to use one or more evaluation tasks must accept an instance of the evaluation task(s) as an argument
-to the constructor and must define a ``validation_step`` and/or ``test_step`` method that calls the ``evaluation_step`` method
-of the evaluation task(s).
-
-
-Creating and Configuring a Project
-----------------------------------
-A project in *mmlearn* can be thought of as a collection of related experiments. Within a project, you can reuse components
-from *mmlearn* (e.g., datasets, models, tasks) or define new ones and use them all together for experiments.
-
-To create a new project, create a new directory following the structure below:
-
-.. code-block:: bash
-
-   my_project/
-   ├── configs/
-   │   ├── __init__.py
-   │   └── experiment/
-   │       ├── my_experiment.yaml
-   ├── README.md (optional)
-   ├── requirements.txt (optional)
-
-The ``configs/`` directory contains all the configurations, both `structured configs <https://hydra.cc/docs/tutorials/structured_config/intro/>`_
-and YAML config files for the experiments in the project. The ``configs/experiment/`` directory contains the `.yaml` files
-for the experiments associated with the project. These `.yaml` files use the `Hydra configuration format <https://hydra.cc/docs/tutorials/basic/your_first_app/composition/>`_,
-which also allows overriding the configuration options/values from the command line.
-
-The ``__init__.py`` file in the ``configs/`` directory is required to make the ``configs/`` directory a Python package,
-allowing hydra to compose configurations from `.yaml` files as well as structured configs from python modules. More on this
-in the next section.
-
-Optionally, you can also include a ``README.md`` file with a brief description of the project and a ``requirements.txt`` file
-with the dependencies required to run the project.
-
-Specifying Configurable Components
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-One of the key features of the Hydra configuration system is the ability to compose configurations from multiple sources,
-including the command line, `.yaml` files and structured configs from Python modules. `Structured Configs <https://hydra.cc/docs/tutorials/structured_config/intro/>`_
-in Hydra use Python :func:`~dataclasses.dataclass` to define the configuration schema. This allows for both static and runtime type-checking
-of the configuration. `Hydra-zen <https://mit-ll-responsible-ai.github.io/hydra-zen/>`_ extends Hydra to makes it easy
-to dynamically generate dataclass-backed configurations for any class or function simply by adding a decorator to the class
-or function.
-
-*mmlearn* provides a pre-populated `config store <https://hydra.cc/docs/tutorials/structured_config/config_store/>`_,
-:py:data:`~mmlearn.conf.external_store`, which can be used as a decorator to register configurable components. This config
-store already contains configurations for common components like PyTorch :py:mod:`optimizers <torch.optim>`,
-:py:mod:`learning rate schedulers <torch.optim.lr_scheduler>`, loss functions and samplers,
-as well as PyTorch Lightning's Trainer :py:mod:`callbacks <lightning.pytorch.callbacks>` and :py:mod:`loggers <lightning.pytorch.loggers>`.
-To dynamically add new configurable components to the store, simply add the :py:data:`~mmlearn.conf.external_store` decorator
-to the class or function definition.
-
-For example, the following code snippet shows how to register a new dataset class:
-
-.. code-block:: python
-
-   from torch.utils.data.dataset import Dataset
-
-   from mmlearn.conf import external_store
-   from mmlearn.constants import EXAMPLE_INDEX_KEY
-   from mmlearn.datasets.core import Example, Modalities
-
-
-   @external_store(group="datasets")
-   class MyMapStyleDataset(Dataset[Example]):
-      ...
-      def __getitem__(self, idx: int) -> Example:
-         ...
-         return Example(
-            {
-               EXAMPLE_INDEX_KEY: idx,
-               Modalities.TEXT.name: ...,
-               Modalities.RGB.name: ...,
-               Modalities.RGB.target: ...,
-               Modalities.TEXT.mask: ...,
-               ...
-            }
-         )
-
-The :py:data:`~mmlearn.conf.external_store` decorator immediately add the class to the config store once the Python interpreter
-loads the module containing the class. This is why the ``configs/`` directory must be a Python package and why modules
-containing user-defined configurable components must be imported in the ``configs/__init__.py`` file.
-
-The ``group`` argument specifies the `config group <https://hydra.cc/docs/tutorials/basic/your_first_app/config_groups/>`_
-under which the configurable component will be registered. This allows users to easily reference the component in the
-configurations using the group name and the class name. The available config groups in *mmlearn* are:
-
-- ``datasets``: Contains all the dataset classes.
-- ``datasets/masking``: Contains all the configurable classes and functions for masking input data.
-- ``datasets/tokenizers``: Contains all the configurable classes and functions for converting raw inputs to tokens.
-- ``datasets/transforms``: Contains all the configurable classes and functions for transforming input data.
-- ``dataloader/sampler``: Contains all the dataloader sampler classes.
-- ``modules/encoders``: Contains all the encoder modules.
-- ``modules/layers``: For layers that can be used independent of the model.
-- ``modules/losses``: Contains all the loss functions.
-- ``modules/optimizers``: Contains all the optimizers.
-- ``modules/lr_schedulers``: Contains all the learning rate schedulers.
-- ``modules/metrics``: Contains all the evaluation metrics.
-- ``tasks``: Contains all the task classes.
-- ``trainer/callbacks``: Contains all the PyTorch Lightning Trainer callbacks.
-- ``trainer/logger``: Contains all the PyTorch Lightning Trainer loggers.
-
-
-The Base Configuration
-~~~~~~~~~~~~~~~~~~~~~~~
-The base configuration for all experiments in *mmlearn* are defined in the :class:`~mmlearn.conf.MMLearnConf`
-dataclass. This serves as the base configuration for all experiments and can be extended to include additional configuration
-options, following Hydra's `override syntax <https://hydra.cc/docs/advanced/override_grammar/basic/>`_.
-
-The base configuration for *mmlearn* is shown below:
-
-.. code-block:: yaml
-
-   experiment_name: ???
-   job_type: train
-   seed: null
-   datasets:
-      train: null
-      val: null
-      test: null
-   dataloader:
-      train:
-         _target_: torch.utils.data.dataloader.DataLoader
-         _convert_: object
-         dataset: ???
-         batch_size: 1
-         shuffle: null
-         sampler: null
-         batch_sampler: null
-         num_workers: 0
-         collate_fn:
-            _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
-            batch_processors: null
-         pin_memory: true
-         drop_last: false
-         timeout: 0.0
-         worker_init_fn: null
-         multiprocessing_context: null
-         generator: null
-         prefetch_factor: null
-         persistent_workers: false
-         pin_memory_device: ''
-      val:
-         _target_: torch.utils.data.dataloader.DataLoader
-         _convert_: object
-         dataset: ???
-         batch_size: 1
-         shuffle: null
-         sampler: null
-         batch_sampler: null
-         num_workers: 0
-         collate_fn:
-            _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
-            batch_processors: null
-         pin_memory: true
-         drop_last: false
-         timeout: 0.0
-         worker_init_fn: null
-         multiprocessing_context: null
-         generator: null
-         prefetch_factor: null
-         persistent_workers: false
-         pin_memory_device: ''
-      test:
-         _target_: torch.utils.data.dataloader.DataLoader
-         _convert_: object
-         dataset: ???
-         batch_size: 1
-         shuffle: null
-         sampler: null
-         batch_sampler: null
-         num_workers: 0
-         collate_fn:
-            _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
-            batch_processors: null
-         pin_memory: true
-         drop_last: false
-         timeout: 0.0
-         worker_init_fn: null
-         multiprocessing_context: null
-         generator: null
-         prefetch_factor: null
-         persistent_workers: false
-         pin_memory_device: ''
-   task: ???
-   trainer:
-      _target_: lightning.pytorch.trainer.trainer.Trainer
-      accelerator: auto
-      strategy: auto
-      devices: auto
-      num_nodes: 1
-      precision: null
-      logger: null
-      callbacks: null
-      fast_dev_run: false
-      max_epochs: null
-      min_epochs: null
-      max_steps: -1
-      min_steps: null
-      max_time: null
-      limit_train_batches: null
-      limit_val_batches: null
-      limit_test_batches: null
-      limit_predict_batches: null
-      overfit_batches: 0.0
-      val_check_interval: null
-      check_val_every_n_epoch: 1
-      num_sanity_val_steps: null
-      log_every_n_steps: null
-      enable_checkpointing: true
-      enable_progress_bar: true
-      enable_model_summary: true
-      accumulate_grad_batches: 1
-      gradient_clip_val: null
-      gradient_clip_algorithm: null
-      deterministic: null
-      benchmark: null
-      inference_mode: true
-      use_distributed_sampler: true
-      profiler: null
-      detect_anomaly: false
-      barebones: false
-      plugins: null
-      sync_batchnorm: false
-      reload_dataloaders_every_n_epochs: 0
-      default_root_dir: ${hydra:runtime.output_dir}/checkpoints
-   tags:
-      - ${experiment_name}
-   resume_from_checkpoint: null
-   strict_loading: true
-   torch_compile_kwargs:
-      disable: true
-      fullgraph: false
-      dynamic: null
-      backend: inductor
-      mode: null
-      options: null
-
-The config keys with a value of ``???`` are placeholders that must be overridden in the experiment configurations. While
-the ``dataset`` key in the ``dataloader`` group is also a placeholder, it should not be provided as it will be automatically
-filled in from the ``datasets`` group.
-
-Configuring an Experiment
-~~~~~~~~~~~~~~~~~~~~~~~~~
-To configure an experiment, create a new `.yaml` file in the ``configs/experiment/`` directory of the project. The configuration
-file should define the experiment-specific configuration options and override the base configuration options as needed.
-Configurable components from the config store can be referenced by name in the configuration file under the
-`defaults list <https://hydra.cc/docs/advanced/defaults_list/>`_. The following code snippet shows an example configuration
-file for an experiment:
-
-.. code-block:: yaml
-
-   # @package _global_
-
-   defaults:
-   - /datasets@datasets.train.my_iterable: MyIterableStyleDataset
-   - /datasets@datasets.train.my_map: MyMapStyleDataset
-   - /modules/encoders@task.encoders.text: MyTextEncoder
-   - /modules/encoders@task.encoders.rgb: MyRGBEncoder
-   - /modules/losses@task.loss: ContrastiveLoss
-   - /modules/optimizers@task.optimizer: AdamW
-   - /modules/lr_schedulers@task.lr_scheduler.scheduler: CosineAnnealingLR
-   - /eval_task@task.evaluation_tasks.retrieval.task: ZeroShotCrossModalRetrieval
-   - /trainer/callbacks@trainer.callbacks.lr_monitor: LearningRateMonitor
-   - /trainer/callbacks@trainer.callbacks.model_checkpoint: ModelCheckpoint
-   - /trainer/callbacks@trainer.callbacks.early_stopping: EarlyStopping
-   - /trainer/callbacks@trainer.callbacks.model_summary: ModelSummary
-   - /trainer/logger@trainer.logger.wandb: WandbLogger
-   - override /task: ContrastivePretraining
-   - _self_
-
-   seed: 42
-
-   datasets:
-      train:
-         my_iterable:
-            my_iterable_arg1: ...
-         my_map:
-            my_map_arg1: ...
-
-   dataloader:
-      train:
-         batch_size: 64
-
-   task:
-      encoders:
-         text:
-            text_arg1: ...
-         rgb:
-            rgb_arg1: ...
-      evaluation_tasks:
-         retrieval:
-            task:
-            task_specs:
-               - query_modality: text
-                  target_modality: rgb
-                  top_k: [10, 200]
-               - query_modality: rgb
-                  target_modality: text
-                  top_k: [10, 200]
-            run_on_validation: false
-            run_on_test: true
-
-Running an Experiment
----------------------
-To run an experiment locally, use the following command:
-
-.. code:: bash
-
-   mmlearn_run 'hydra.searchpath=[pkg://path.to.my_project.configs]' \
-      +experiment=my_experiment \
-      experiment_name=my_experiment_name
-
-.. tip::
-   You can see the full config for an experiment without running it by adding the ``--help`` flag to the command.
-
-   .. code:: bash
-
-      mmlearn_run 'hydra.searchpath=[pkg://path.to.my_project.configs]' \
-         +experiment=my_experiment \
-         experiment_name=my_experiment_name \
-         task=my_task \ # required for the command to run
-         --help
-
-To run the experiment on a SLURM cluster, use the following command:
-
-.. code:: bash
-
-   mmlearn_run --multirun \
-      hydra.launcher.mem_per_cpu=5G \
-      hydra.launcher.qos=your_qos \
-      hydra.launcher.partition=your_partition \
-      hydra.launcher.gres=gpu:4 \
-      hydra.launcher.cpus_per_task=8 \
-      hydra.launcher.tasks_per_node=4 \
-      hydra.launcher.nodes=1 \
-      hydra.launcher.stderr_to_stdout=true \
-      hydra.launcher.timeout_min=720 \
-      'hydra.searchpath=[pkg://path.to.my_project.configs]' \
-      +experiment=my_experiment \
-      experiment_name=my_experiment_name
-
-This uses the `submitit launcher <https://hydra.cc/docs/plugins/submitit_launcher/>`_ plugin built into Hydra to submit
-the experiment to the SLURM scheduler with the specified resources.
-
-.. note::
-   After the job is submitted, it is okay to cancel the program with ``Ctrl+C``. The job will continue running on
-   the cluster. You can also add ``&`` at the end of the command to run it in the background.
diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css
new file mode 100644
index 0000000..4ab12ac
--- /dev/null
+++ b/docs/stylesheets/extra.css
@@ -0,0 +1,91 @@
+[data-md-color-primary="vector"] {
+  --md-primary-fg-color: #eb088a;
+  --md-primary-fg-color--light: #f252a5;
+  --md-primary-fg-color--dark: #b00068;
+  --md-primary-bg-color: hsla(0, 0%, 100%, 1);
+  --md-primary-bg-color--light: hsla(0, 0%, 100%, 0.7);
+}
+
+[data-md-color-primary="black"] {
+  --md-primary-fg-color: #181818;
+  --md-primary-fg-color--light: #f252a5;
+  --md-primary-fg-color--dark: #b00068;
+  --md-primary-bg-color: #eb088a;
+}
+
+[data-md-color-accent="vector-teal"] {
+  --md-accent-fg-color: #48c0d9;
+  --md-accent-fg-color--transparent: #526cfe1a;
+  --md-accent-bg-color: #fff;
+  --md-accent-bg-color--light: #ffffffb3;
+}
+
+[data-md-color-scheme="slate"][data-md-color-primary="black"] {
+  --md-typeset-a-color: #eb088a;
+}
+
+[data-md-color-scheme="default"] {
+  /* Default light mode styling */
+}
+
+[data-md-color-scheme="slate"] {
+  --md-typeset-a-color: #eb088a;
+  /* Dark mode styling */
+}
+
+/* Vector logo css styling to match overrides/partial/copyright.html */
+.md-footer-vector {
+  display: flex;
+  align-items: center;
+  padding: 0 0.6rem;
+}
+
+.md-footer-vector img {
+  height: 24px; /* Reduce height to a fixed value */
+  width: auto; /* Maintain aspect ratio */
+  transition: opacity 0.25s;
+  opacity: 0.7;
+}
+
+.md-footer-vector img:hover {
+  opacity: 1;
+}
+
+/* Make the inner footer grid elements distribute evenly */
+.md-footer-meta__inner {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+}
+
+/* To make socials and Vector logo not stack when viewing on mobile */
+@media screen and (max-width: 76.234375em) {
+  .md-footer-meta__inner.md-grid {
+    flex-direction: row;
+    justify-content: space-between;
+    align-items: center;
+  }
+
+  .md-copyright,
+  .md-social {
+    width: auto;
+    max-width: 49%;
+  }
+
+  /* Prevent margin that causes stacking */
+  .md-social {
+    margin: 0;
+  }
+}
+
+/* Reduce margins for h2 when using grid cards */
+.grid.cards h2 {
+  margin-top: 0; /* Remove top margin completely in cards */
+  margin-bottom: 0.5rem; /* Smaller bottom margin in cards */
+}
+
+.vector-icon {
+  color: #eb088a;
+  opacity: 0.7;
+  margin-right: 0.2em;
+}
diff --git a/docs/user_guide.md b/docs/user_guide.md
new file mode 100644
index 0000000..c61e7bc
--- /dev/null
+++ b/docs/user_guide.md
@@ -0,0 +1,582 @@
+# User Guide
+
+*mmlearn* contains a collection of tools and utilities to help researchers and practitioners easily set up and run training
+or evaluation experiments for multimodal representation learning methods. The toolkit is designed to be modular and extensible.
+We aim to provide a high degree of flexibility in using existing methods, while also allowing users to easily add support
+for new modalities of data, datasets, models and pretraining or evaluation methods.
+
+Much of the power and flexibility of *mmlearn* comes from building on top of the [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)
+framework and using [Hydra](https://hydra.cc/docs/intro/) and [hydra-zen](https://mit-ll-responsible-ai.github.io/hydra-zen/)
+for configuration management. Together, these tools make it easy to define and run experiments with different configurations,
+and to scale up experiments to run on a SLURM cluster.
+
+The goal of this guide is to give you a brief overview of what *mmlearn* is and how you can get started using it.
+
+!!! note
+    *mmlearn* currently only supports training and evaluation of encoder-only models.
+
+    For more detailed information on the features and capabilities of *mmlearn*, please refer to the [API Reference](api.md).
+
+## Defining a Dataset
+
+Datasets in *mmlearn* can be defined using PyTorch's `torch.utils.data.Dataset` or `torch.utils.data.IterableDataset`
+classes. However, there are two additional requirements for datasets in *mmlearn*:
+
+1. The dataset must return an instance of `mmlearn.datasets.core.example.Example` from the `__getitem__`
+   method or the `__iter__` method.
+2. The `Example` object returned by the dataset must contain the key `'example_index'`
+   and use modality-specific keys from the `Modalities` registry
+   to store the data.
+
+**Example 1**: Defining a map-style dataset in *mmlearn*:
+
+```python
+from torch.utils.data.dataset import Dataset
+
+from mmlearn.datasets.core import Example, Modalities
+from mmlearn.constants import EXAMPLE_INDEX_KEY
+
+
+class MyMapStyleDataset(Dataset[Example]):
+   ...
+   def __getitem__(self, idx: int) -> Example:
+      ...
+      return Example(
+         {
+            EXAMPLE_INDEX_KEY: idx,
+            Modalities.TEXT.name: ...,
+            Modalities.RGB.name: ...,
+            Modalities.RGB.target: ...,
+            Modalities.TEXT.mask: ...,
+            ...
+         }
+      )
+```
+
+**Example 2**: Defining an iterable-style dataset in *mmlearn*:
+
+```python
+from torch.utils.data.dataset import IterableDataset
+
+from mmlearn.datasets.core import Example, Modalities
+from mmlearn.constants import EXAMPLE_INDEX_KEY
+
+
+class MyIterableStyleDataset(IterableDataset[Example]):
+   ...
+   def __iter__(self) -> Generator[Example, None, None]:
+      ...
+      idx = 0
+      for item in items:
+         yield Example(
+            {
+               EXAMPLE_INDEX_KEY: idx,
+               Modalities.TEXT.name: ...,
+               Modalities.AUDIO.name: ...,
+               Modalities.TEXT.mask: ...,
+               Modalities.AUDIO.mask: ...,
+               ...
+            }
+         )
+         idx += 1
+```
+
+The `Example` class represents a single example in the dataset and all the attributes
+associated with it. The class is an extension of the `collections.OrderedDict` class that provides attribute-style access
+to the dictionary values and handles the creation of the `'example_ids'` tuple, combining the `'example_index'` and
+`'dataset_index'` values. The `'example_index'` key is created by the dataset object for each example returned by the
+dataset. On the other hand, the `'dataset_index'` key is created by the `CombinedDataset`
+each `Example` object returned by the dataset.
+
+!!! note
+    All dataset objects in *mmlearn* are wrapped in the `CombinedDataset` class,
+    which is a subclass of `torch.utils.data.Dataset`. As such, the user almost never has to add/define the `'dataset_index'`
+    key explicitly.
+
+    Since batching typically combines data from the same modality into one tensor, both the `'example_index'` and `'dataset_index'`
+    keys are essential for uniquely identifying paired examples across different modalities from the same dataset. The
+    `find_matching_indices` function does exactly this by finding the indices of the
+    examples in a batch that have the same `'example_ids'` tuple.
+
+`Modalities` is an instance of `ModalityRegistry`
+singleton class that serves as a global registry for all the modalities supported by *mmlearn*. It allows dot-style access
+registered modalities and their properties. For example, the `'RGB'` modality can be accessed using `Modalities.RGB`
+(returns string `'rgb'`) and the `'target'` property of the `'RGB'` modality can be accessed using `Modalities.RGB.target`
+(returns the string `'rgb_target'`). It also provides a method to register new modalities and their properties. For example,
+the following code snippet shows how to register a new `'DNA'` modality:
+
+```python
+from mmlearn.datasets.core import Modalities
+
+Modalities.register_modality("dna")
+```
+
+## Adding New Modules
+
+Modules are building blocks for models and tasks in *mmlearn*. They can be anything from encoders, layers, losses, optimizers,
+learning rate schedulers, metrics, etc. Modules in *mmlearn* are generally defined by extending PyTorch's `torch.nn.Module`
+class.
+
+Users have the flexibility to design new modules according to their requirements, with the exception of encoder modules
+and modules associated with specific pre-defined tasks (e.g., loss functions for the `ContrastivePretraining` task).
+The forward method of encoder modules must accept a dictionary as input, where the keys are the names of the modalities
+and the values are the corresponding (batched) tensors/data. This format makes it easier to reuse the encoder with different
+modalities and different tasks. In addition, the forward method must return a list-like object where the first element is
+the last layer's output. The following code snippet shows how to define a new text encoder module:
+
+```python
+import torch
+from torch import nn
+
+from mmlearn.datasets.core import Modalities
+
+
+class MyTextEncoder(nn.Module):
+   def __init__(self, input_dim: int, output_dim: int):
+      super().__init__()
+      self.encoder = ...
+
+   def forward(self, inputs: dict[str, torch.Tensor]) -> tuple[torch.Tensor]:
+      out = self.encoder(
+         inputs[Modalities.TEXT.name],
+         inputs.get(
+            "attention_mask", inputs.get(Modalities.TEXT.attention_mask, None)
+         ),
+      )
+      return (out,)
+```
+
+For modules associated with pre-defined tasks, the new modules must adhere to the same function signature as the existing
+modules for that task. For instance, the forward method of a new loss function for the `ContrastivePretraining`
+task must have the following signature to be compatible with the existing loss functions for the task:
+
+```python
+import torch
+
+from mmlearn.tasks.contrastive_pretraining import LossPairSpec
+
+def my_contrastive_loss(
+   embeddings: dict[str, torch.Tensor],
+   example_ids: dict[str, torch.Tensor],
+   logit_scale: torch.Tensor,
+   modality_loss_pairs: list[LossPairSpec],
+) -> torch.Tensor:
+   ...
+```
+
+## Adding New Tasks
+
+Tasks in *mmlearn* represent the different training and/or evaluation objectives that can be performed on the data using
+the different modules. Tasks that require training should extend the `TrainingTask` class, while tasks
+involving only evaluation should extend the `EvaluationHooks` class.
+
+### Training Tasks
+
+The `TrainingTask` class is an extension of the `lightning.pytorch.core.LightningModule`
+class, which itself is an extension of the `torch.nn.Module` class. The class provides a common interface for training
+tasks in *mmlearn*. It allows users to define the training loop, validation loop, test loop, and the setup for the model,
+optimizer, learning rate scheduler and loss function, all in one place (a functionality inherited from PyTorch Lightning).
+The class also provides hooks for customizing the training loop, validation loop, and test loop, as well as a suite of
+other functionalities like logging, checkpointing and handling distributed training.
+
+!!! seealso
+    For more information on the features and capabilities of the `TrainingTask` class inherited
+    from PyTorch Lightning, please refer to the PyTorch Lightning [documentation](https://lightning.ai/docs/pytorch/stable/).
+
+To be used with the PyTorch Lightning Trainer, extensions of the `TrainingTask` class must define
+a `training_step` method. The following code snippet shows the minimum requirements for defining a new task in *mmlearn*:
+
+```python
+from typing import Any, Optional, Union
+from functools import partial
+
+import torch
+
+from mmlearn.tasks.base import TrainingTask
+
+class MyTask(TrainingTask):
+   def __init__(
+      self,
+      optimizer: Optional[partial[torch.optim.Optimizer]],
+      loss_fn: Optional[torch.nn.Module],
+      lr_scheduler: Optional[
+         Union[
+            dict[str, Union[partial[torch.optim.lr_scheduler.LRScheduler], Any]],
+            partial[torch.optim.lr_scheduler.LRScheduler],
+         ]
+      ] = None,
+   ) -> None:
+      super().__init__(optimizer=optimizer, loss_fn=loss_fn, lr_scheduler=lr_scheduler)
+
+      # Since this class also inherits from torch.nn.Module, we can define the
+      # model and its components directly in the constructor and also define
+      # a forward method for the model as an instance method of this class.
+      # Alternatively, we can pass the model as an argument to the constructor
+      # and assign it to an instance variable.
+      self.model = ...
+
+   def training_step(self, batch: dict[str, Any], batch_idx: int) -> torch.Tensor:
+      outputs = self.model(batch) # or self(batch) if a forward method is defined in this class
+
+      # maybe process outputs here
+
+      loss = self.loss_fn(outputs, ...)
+      return loss
+```
+
+### Evaluation Tasks
+
+The `EvaluationHooks` class is intended to be used for evaluation tasks that don't require training,
+e.g. zero-shot evaluation tasks (as opposed to evaluation tasks like linear probing, which require training). The class provides
+an interface for defining and customizing the evaluation loop.
+
+Classes that inherit from `EvaluationHooks` cannot be run/used on their own. They must be used
+in conjunction with a training task, which will call the hooks defined in the evaluation task during the evaluation phase.
+This way, multiple evaluation tasks can be defined and used with the same training task. The model to be evaluated is
+provided by the training task to the evaluation task.
+
+Training tasks that wish to use one or more evaluation tasks must accept an instance of the evaluation task(s) as an argument
+to the constructor and must define a `validation_step` and/or `test_step` method that calls the `evaluation_step` method
+of the evaluation task(s).
+
+## Creating and Configuring a Project
+
+A project in *mmlearn* can be thought of as a collection of related experiments. Within a project, you can reuse components
+from *mmlearn* (e.g., datasets, models, tasks) or define new ones and use them all together for experiments.
+
+To create a new project, create a new directory following the structure below:
+
+```bash
+my_project/
+├── configs/
+│   ├── __init__.py
+│   └── experiment/
+│       ├── my_experiment.yaml
+├── README.md (optional)
+├── requirements.txt (optional)
+```
+
+The `configs/` directory contains all the configurations, both [structured configs](https://hydra.cc/docs/tutorials/structured_config/intro/)
+and YAML config files for the experiments in the project. The `configs/experiment/` directory contains the `.yaml` files
+for the experiments associated with the project. These `.yaml` files use the [Hydra configuration format](https://hydra.cc/docs/tutorials/basic/your_first_app/composition/),
+which also allows overriding the configuration options/values from the command line.
+
+The `__init__.py` file in the `configs/` directory is required to make the `configs/` directory a Python package,
+allowing hydra to compose configurations from `.yaml` files as well as structured configs from python modules. More on this
+in the next section.
+
+Optionally, you can also include a `README.md` file with a brief description of the project and a `requirements.txt` file
+with the dependencies required to run the project.
+
+### Specifying Configurable Components
+
+One of the key features of the Hydra configuration system is the ability to compose configurations from multiple sources,
+including the command line, `.yaml` files and structured configs from Python modules. [Structured Configs](https://hydra.cc/docs/tutorials/structured_config/intro/)
+in Hydra use Python `dataclasses.dataclass` to define the configuration schema. This allows for both static and runtime type-checking
+of the configuration. [Hydra-zen](https://mit-ll-responsible-ai.github.io/hydra-zen/) extends Hydra to makes it easy
+to dynamically generate dataclass-backed configurations for any class or function simply by adding a decorator to the class
+or function.
+
+*mmlearn* provides a pre-populated [config store](https://hydra.cc/docs/tutorials/structured_config/config_store/),
+`mmlearn.conf.external_store`, which can be used as a decorator to register configurable components. This config
+store already contains configurations for common components like PyTorch optimizers,
+learning rate schedulers, loss functions and samplers,
+as well as PyTorch Lightning's Trainer callbacks and loggers.
+To dynamically add new configurable components to the store, simply add the `mmlearn.conf.external_store` decorator
+to the class or function definition.
+
+For example, the following code snippet shows how to register a new dataset class:
+
+```python
+from torch.utils.data.dataset import Dataset
+
+from mmlearn.conf import external_store
+from mmlearn.constants import EXAMPLE_INDEX_KEY
+from mmlearn.datasets.core import Example, Modalities
+
+
+@external_store(group="datasets")
+class MyMapStyleDataset(Dataset[Example]):
+   ...
+   def __getitem__(self, idx: int) -> Example:
+      ...
+      return Example(
+         {
+            EXAMPLE_INDEX_KEY: idx,
+            Modalities.TEXT.name: ...,
+            Modalities.RGB.name: ...,
+            Modalities.RGB.target: ...,
+            Modalities.TEXT.mask: ...,
+            ...
+         }
+      )
+```
+
+The `mmlearn.conf.external_store` decorator immediately add the class to the config store once the Python interpreter
+loads the module containing the class. This is why the `configs/` directory must be a Python package and why modules
+containing user-defined configurable components must be imported in the `configs/__init__.py` file.
+
+The `group` argument specifies the [config group](https://hydra.cc/docs/tutorials/basic/your_first_app/config_groups/)
+under which the configurable component will be registered. This allows users to easily reference the component in the
+configurations using the group name and the class name. The available config groups in *mmlearn* are:
+
+- `datasets`: Contains all the dataset classes.
+- `datasets/masking`: Contains all the configurable classes and functions for masking input data.
+- `datasets/tokenizers`: Contains all the configurable classes and functions for converting raw inputs to tokens.
+- `datasets/transforms`: Contains all the configurable classes and functions for transforming input data.
+- `dataloader/sampler`: Contains all the dataloader sampler classes.
+- `modules/encoders`: Contains all the encoder modules.
+- `modules/layers`: For layers that can be used independent of the model.
+- `modules/losses`: Contains all the loss functions.
+- `modules/optimizers`: Contains all the optimizers.
+- `modules/lr_schedulers`: Contains all the learning rate schedulers.
+- `modules/metrics`: Contains all the evaluation metrics.
+- `tasks`: Contains all the task classes.
+- `trainer/callbacks`: Contains all the PyTorch Lightning Trainer callbacks.
+- `trainer/logger`: Contains all the PyTorch Lightning Trainer loggers.
+
+### The Base Configuration
+
+The base configuration for all experiments in *mmlearn* are defined in the `MMLearnConf`
+dataclass. This serves as the base configuration for all experiments and can be extended to include additional configuration
+options, following Hydra's [override syntax](https://hydra.cc/docs/advanced/override_grammar/basic/).
+
+The base configuration for *mmlearn* is shown below:
+
+```yaml
+experiment_name: ???
+job_type: train
+seed: null
+datasets:
+   train: null
+   val: null
+   test: null
+dataloader:
+   train:
+      _target_: torch.utils.data.dataloader.DataLoader
+      _convert_: object
+      dataset: ???
+      batch_size: 1
+      shuffle: null
+      sampler: null
+      batch_sampler: null
+      num_workers: 0
+      collate_fn:
+         _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
+         batch_processors: null
+      pin_memory: true
+      drop_last: false
+      timeout: 0.0
+      worker_init_fn: null
+      multiprocessing_context: null
+      generator: null
+      prefetch_factor: null
+      persistent_workers: false
+      pin_memory_device: ''
+   val:
+      _target_: torch.utils.data.dataloader.DataLoader
+      _convert_: object
+      dataset: ???
+      batch_size: 1
+      shuffle: null
+      sampler: null
+      batch_sampler: null
+      num_workers: 0
+      collate_fn:
+         _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
+         batch_processors: null
+      pin_memory: true
+      drop_last: false
+      timeout: 0.0
+      worker_init_fn: null
+      multiprocessing_context: null
+      generator: null
+      prefetch_factor: null
+      persistent_workers: false
+      pin_memory_device: ''
+   test:
+      _target_: torch.utils.data.dataloader.DataLoader
+      _convert_: object
+      dataset: ???
+      batch_size: 1
+      shuffle: null
+      sampler: null
+      batch_sampler: null
+      num_workers: 0
+      collate_fn:
+         _target_: mmlearn.datasets.core.data_collator.DefaultDataCollator
+         batch_processors: null
+      pin_memory: true
+      drop_last: false
+      timeout: 0.0
+      worker_init_fn: null
+      multiprocessing_context: null
+      generator: null
+      prefetch_factor: null
+      persistent_workers: false
+      pin_memory_device: ''
+task: ???
+trainer:
+   _target_: lightning.pytorch.trainer.trainer.Trainer
+   accelerator: auto
+   strategy: auto
+   devices: auto
+   num_nodes: 1
+   precision: null
+   logger: null
+   callbacks: null
+   fast_dev_run: false
+   max_epochs: null
+   min_epochs: null
+   max_steps: -1
+   min_steps: null
+   max_time: null
+   limit_train_batches: null
+   limit_val_batches: null
+   limit_test_batches: null
+   limit_predict_batches: null
+   overfit_batches: 0.0
+   val_check_interval: null
+   check_val_every_n_epoch: 1
+   num_sanity_val_steps: null
+   log_every_n_steps: null
+   enable_checkpointing: true
+   enable_progress_bar: true
+   enable_model_summary: true
+   accumulate_grad_batches: 1
+   gradient_clip_val: null
+   gradient_clip_algorithm: null
+   deterministic: null
+   benchmark: null
+   inference_mode: true
+   use_distributed_sampler: true
+   profiler: null
+   detect_anomaly: false
+   barebones: false
+   plugins: null
+   sync_batchnorm: false
+   reload_dataloaders_every_n_epochs: 0
+   default_root_dir: ${hydra:runtime.output_dir}/checkpoints
+tags:
+   - ${experiment_name}
+resume_from_checkpoint: null
+strict_loading: true
+torch_compile_kwargs:
+   disable: true
+   fullgraph: false
+   dynamic: null
+   backend: inductor
+   mode: null
+   options: null
+```
+
+The config keys with a value of `???` are placeholders that must be overridden in the experiment configurations. While
+the `dataset` key in the `dataloader` group is also a placeholder, it should not be provided as it will be automatically
+filled in from the `datasets` group.
+
+### Configuring an Experiment
+
+To configure an experiment, create a new `.yaml` file in the `configs/experiment/` directory of the project. The configuration
+file should define the experiment-specific configuration options and override the base configuration options as needed.
+Configurable components from the config store can be referenced by name in the configuration file under the
+[defaults list](https://hydra.cc/docs/advanced/defaults_list/). The following code snippet shows an example configuration
+file for an experiment:
+
+```yaml
+# @package _global_
+
+defaults:
+- /datasets@datasets.train.my_iterable: MyIterableStyleDataset
+- /datasets@datasets.train.my_map: MyMapStyleDataset
+- /modules/encoders@task.encoders.text: MyTextEncoder
+- /modules/encoders@task.encoders.rgb: MyRGBEncoder
+- /modules/losses@task.loss: ContrastiveLoss
+- /modules/optimizers@task.optimizer: AdamW
+- /modules/lr_schedulers@task.lr_scheduler.scheduler: CosineAnnealingLR
+- /eval_task@task.evaluation_tasks.retrieval.task: ZeroShotCrossModalRetrieval
+- /trainer/callbacks@trainer.callbacks.lr_monitor: LearningRateMonitor
+- /trainer/callbacks@trainer.callbacks.model_checkpoint: ModelCheckpoint
+- /trainer/callbacks@trainer.callbacks.early_stopping: EarlyStopping
+- /trainer/callbacks@trainer.callbacks.model_summary: ModelSummary
+- /trainer/logger@trainer.logger.wandb: WandbLogger
+- override /task: ContrastivePretraining
+- _self_
+
+seed: 42
+
+datasets:
+   train:
+      my_iterable:
+         my_iterable_arg1: ...
+      my_map:
+         my_map_arg1: ...
+
+dataloader:
+   train:
+      batch_size: 64
+
+task:
+   encoders:
+      text:
+         text_arg1: ...
+      rgb:
+         rgb_arg1: ...
+   evaluation_tasks:
+      retrieval:
+         task:
+         task_specs:
+            - query_modality: text
+               target_modality: rgb
+               top_k: [10, 200]
+            - query_modality: rgb
+               target_modality: text
+               top_k: [10, 200]
+         run_on_validation: false
+         run_on_test: true
+```
+
+## Running an Experiment
+
+To run an experiment locally, use the following command:
+
+```bash
+mmlearn_run 'hydra.searchpath=[pkg://path.to.my_project.configs]' \
+   +experiment=my_experiment \
+   experiment_name=my_experiment_name
+```
+
+!!! tip
+    You can see the full config for an experiment without running it by adding the `--help` flag to the command.
+
+    ```bash
+    mmlearn_run 'hydra.searchpath=[pkg://path.to.my_project.configs]' \
+       +experiment=my_experiment \
+       experiment_name=my_experiment_name \
+       task=my_task \ # required for the command to run
+       --help
+    ```
+
+To run the experiment on a SLURM cluster, use the following command:
+
+```bash
+mmlearn_run --multirun \
+   hydra.launcher.mem_per_cpu=5G \
+   hydra.launcher.qos=your_qos \
+   hydra.launcher.partition=your_partition \
+   hydra.launcher.gres=gpu:4 \
+   hydra.launcher.cpus_per_task=8 \
+   hydra.launcher.tasks_per_node=4 \
+   hydra.launcher.nodes=1 \
+   hydra.launcher.stderr_to_stdout=true \
+   hydra.launcher.timeout_min=720 \
+   'hydra.searchpath=[pkg://path.to.my_project.configs]' \
+   +experiment=my_experiment \
+   experiment_name=my_experiment_name
+```
+
+This uses the [submitit launcher](https://hydra.cc/docs/plugins/submitit_launcher/) plugin built into Hydra to submit
+the experiment to the SLURM scheduler with the specified resources.
+
+!!! note
+    After the job is submitted, it is okay to cancel the program with `Ctrl+C`. The job will continue running on
+    the cluster. You can also add `&` at the end of the command to run it in the background.
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..9c48018
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,85 @@
+extra_css:
+  - stylesheets/extra.css
+extra:
+  generator: false
+  social:
+    - icon: fontawesome/brands/github
+      link: https://github.com/VectorInstitute/mmlearn
+markdown_extensions:
+  - attr_list
+  - admonition
+  - md_in_html
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.inlinehilite
+  - pymdownx.details
+  - pymdownx.snippets
+  - pymdownx.superfences
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+  - toc:
+      permalink: true
+nav:
+  - Home: index.md
+  - Installation: installation.md
+  - User Guide: user_guide.md
+  - API Reference: api.md
+  - Contributing: contributing.md
+plugins:
+  - search
+  - autorefs
+  - include-markdown
+  - mkdocstrings:
+      handlers:
+        python:
+          options:
+            docstring_style: numpy
+            members_order: source
+            separate_signature: true
+            show_overloads: true
+            show_submodules: true
+            show_root_heading: false
+            show_root_full_path: true
+            show_root_toc_entry: false
+            show_symbol_type_heading: true
+            show_symbol_type_toc: true
+repo_url: https://github.com/VectorInstitute/mmlearn
+repo_name: VectorInstitute/mmlearn
+site_name: mmlearn
+theme:
+  custom_dir: docs/overrides
+  favicon: assets/favicon-48x48.svg
+  features:
+    - content.code.annotate
+    - content.code.copy
+    - navigation.footer
+    - navigation.indexes
+    - navigation.instant
+    - navigation.tabs
+    - navigation.top
+    - search.suggest
+    - search.highlight
+    - toc.follow
+  icon:
+    repo: fontawesome/brands/github
+  logo: assets/vector-logo.svg
+  logo_footer: assets/vector-logo.svg
+  name: material
+  palette:
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      primary: vector
+      accent: vector-teal
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      primary: black
+      accent: vector-teal
+      toggle:
+        icon: material/brightness-4
+        name: Switch to light mode
diff --git a/mmlearn/datasets/chexpert.py b/mmlearn/datasets/chexpert.py
index 34bc480..ff14961 100644
--- a/mmlearn/datasets/chexpert.py
+++ b/mmlearn/datasets/chexpert.py
@@ -46,7 +46,7 @@ class CheXpert(Dataset[Example]):
 
     Parameters
     ----------
-    data_root : str
+    root_dir : str
         Directory which contains `.json` files stating all dataset entries.
     split : {"train", "valid"}
         Dataset split.
diff --git a/mmlearn/datasets/nyuv2.py b/mmlearn/datasets/nyuv2.py
index 9bc54a9..9a871d8 100644
--- a/mmlearn/datasets/nyuv2.py
+++ b/mmlearn/datasets/nyuv2.py
@@ -85,8 +85,8 @@ class NYUv2Dataset(Dataset[Example]):
         - `"disparity"`: Return the depth image as disparity map.
         - `"image"`: Return the depth image as a 3-channel image.
     rgb_transform: Callable[[PIL.Image], torch.Tensor], default=None
-       A callable that takes in an RGB PIL image and returns a transformed version
-       of the image as a PyTorch tensor.
+        A callable that takes in an RGB PIL image and returns a transformed version
+        of the image as a PyTorch tensor.
     depth_transform: Callable[[PIL.Image], torch.Tensor], default=None
         A callable that takes in a depth PIL image and returns a transformed version
         of the image as a PyTorch tensor.
diff --git a/mmlearn/datasets/sunrgbd.py b/mmlearn/datasets/sunrgbd.py
index 30befe0..50e8f07 100644
--- a/mmlearn/datasets/sunrgbd.py
+++ b/mmlearn/datasets/sunrgbd.py
@@ -124,8 +124,8 @@ class SUNRGBDDataset(Dataset[Example]):
         converted to disparity similar to the ImageBind implementation.
         Otherwise, return the depth image as a 3-channel image.
     rgb_transform: Callable[[PIL.Image], torch.Tensor], default=None
-       A callable that takes in an RGB PIL image and returns a transformed version
-       of the image as a PyTorch tensor.
+        A callable that takes in an RGB PIL image and returns a transformed version
+        of the image as a PyTorch tensor.
     depth_transform: Callable[[PIL.Image], torch.Tensor], default=None
         A callable that takes in a depth PIL image and returns a transformed version
         of the image as a PyTorch tensor.
diff --git a/mmlearn/modules/encoders/vision.py b/mmlearn/modules/encoders/vision.py
index d882ac8..b525190 100644
--- a/mmlearn/modules/encoders/vision.py
+++ b/mmlearn/modules/encoders/vision.py
@@ -215,8 +215,6 @@ class VisionTransformer(nn.Module):
         Normalization layer to use.
     init_std : float, optional, default=0.02
         Standard deviation for weight initialization.
-    **kwargs : dict
-        Additional keyword arguments.
     """
 
     def __init__(
@@ -430,8 +428,6 @@ class VisionTransformerPredictor(nn.Module):
         Normalization layer to use.
     init_std : float, optional, default=0.02
         Standard deviation for weight initialization.
-    **kwargs : dict
-        Additional keyword arguments.
     """
 
     def __init__(
diff --git a/mmlearn/modules/layers/logit_scaling.py b/mmlearn/modules/layers/logit_scaling.py
index 74ddd3e..437110d 100644
--- a/mmlearn/modules/layers/logit_scaling.py
+++ b/mmlearn/modules/layers/logit_scaling.py
@@ -12,7 +12,7 @@ class LearnableLogitScaling(torch.nn.Module):
 
     Parameters
     ----------
-    logit_scale_init : float, optional, default=1/0.07
+    init_logit_scale : float, optional, default=1/0.07
         Initial value of the logit scale.
     learnable : bool, optional, default=True
         If True, the logit scale is learnable. Otherwise, it is fixed.
diff --git a/pyproject.toml b/pyproject.toml
index 345d37c..3e76f50 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -50,19 +50,17 @@ dev = [
 ]
 
 docs = [
+    "mkdocs>=1.6.0,<2.0.0",
+    "mkdocs-material>=9.5.13,<10.0.0",
+    "mkdocstrings>=0.25.0,<0.26.0",
+    "mkdocstrings-python>=1.9.0,<2.0.0",
+    "mkdocs-autorefs>=1.0.0,<2.0.0",
+    "pymdown-extensions>=10.9.0,<11.0.0",
+    "mkdocs-include-markdown-plugin>=6.0.4,<7.0.0",
     "numpydoc>=1.8.0,<2.0.0",
-    "sphinx>=8.1.3,<9.0.0",
-    "sphinxcontrib-apidoc>=0.5.0,<0.6.0",
-    "sphinx-autodoc-typehints>=2.5.0,<3.0.0",
-    "myst-parser>=4.0.0,<5.0.0",
-    "sphinx-design>=0.6.1,<0.7.0",
-    "sphinx-copybutton>=0.5.2,<0.6.0",
-    "sphinx-autoapi>=3.4.0,<4.0.0",
-    "nbsphinx>=0.9.5,<0.10.0",
     "ipython>=8.32.0,<9.0.0",
     "ipykernel>=6.29.5,<7.0.0",
-    "furo>=2024.8.6,<2025.0.0",
-    "sphinx-autobuild==2024.10.3"
+    "black>=24.3.0,<25.0.0"
 ]
 
 [project.scripts]
diff --git a/uv.lock b/uv.lock
index b66c5d9..2be6297 100644
--- a/uv.lock
+++ b/uv.lock
@@ -144,21 +144,6 @@ version = "4.9.3"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
 
-[[package]]
-name = "anyio"
-version = "4.8.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "exceptiongroup", marker = "python_full_version < '3.11'" },
-    { name = "idna" },
-    { name = "sniffio" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/a3/73/199a98fc2dae33535d6b8e8e6ec01f8c1d76c9adb096c6b7d64823038cde/anyio-4.8.0.tar.gz", hash = "sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a", size = 181126 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/46/eb/e7f063ad1fec6b3178a3cd82d1a3c4de82cccf283fc42746168188e1cdd5/anyio-4.8.0-py3-none-any.whl", hash = "sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a", size = 96041 },
-]
-
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -168,18 +153,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321 },
 ]
 
-[[package]]
-name = "astroid"
-version = "3.3.8"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/80/c5/5c83c48bbf547f3dd8b587529db7cf5a265a3368b33e85e76af8ff6061d3/astroid-3.3.8.tar.gz", hash = "sha256:a88c7994f914a4ea8572fac479459f4955eeccc877be3f2d959a33273b0cf40b", size = 398196 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/07/28/0bc8a17d6cd4cc3c79ae41b7105a2b9a327c110e5ddd37a8a27b29a5c8a2/astroid-3.3.8-py3-none-any.whl", hash = "sha256:187ccc0c248bfbba564826c26f070494f7bc964fd286b6d9fff4420e55de828c", size = 275153 },
-]
-
 [[package]]
 name = "asttokens"
 version = "3.0.0"
@@ -217,33 +190,45 @@ wheels = [
 ]
 
 [[package]]
-name = "beautifulsoup4"
-version = "4.13.3"
+name = "backrefs"
+version = "5.8"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "soupsieve" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f0/3c/adaf39ce1fb4afdd21b611e3d530b183bb7759c9b673d60db0e347fd4439/beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b", size = 619516 }
+sdist = { url = "https://files.pythonhosted.org/packages/6c/46/caba1eb32fa5784428ab401a5487f73db4104590ecd939ed9daaf18b47e0/backrefs-5.8.tar.gz", hash = "sha256:2cab642a205ce966af3dd4b38ee36009b31fa9502a35fd61d59ccc116e40a6bd", size = 6773994 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/f9/49/6abb616eb3cbab6a7cca303dc02fdf3836de2e0b834bf966a7f5271a34d8/beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16", size = 186015 },
+    { url = "https://files.pythonhosted.org/packages/bf/cb/d019ab87fe70e0fe3946196d50d6a4428623dc0c38a6669c8cae0320fbf3/backrefs-5.8-py310-none-any.whl", hash = "sha256:c67f6638a34a5b8730812f5101376f9d41dc38c43f1fdc35cb54700f6ed4465d", size = 380337 },
+    { url = "https://files.pythonhosted.org/packages/a9/86/abd17f50ee21b2248075cb6924c6e7f9d23b4925ca64ec660e869c2633f1/backrefs-5.8-py311-none-any.whl", hash = "sha256:2e1c15e4af0e12e45c8701bd5da0902d326b2e200cafcd25e49d9f06d44bb61b", size = 392142 },
+    { url = "https://files.pythonhosted.org/packages/b3/04/7b415bd75c8ab3268cc138c76fa648c19495fcc7d155508a0e62f3f82308/backrefs-5.8-py312-none-any.whl", hash = "sha256:bbef7169a33811080d67cdf1538c8289f76f0942ff971222a16034da88a73486", size = 398021 },
+    { url = "https://files.pythonhosted.org/packages/0c/37/fb6973edeb700f6e3d6ff222400602ab1830446c25c7b4676d8de93e65b8/backrefs-5.8-py39-none-any.whl", hash = "sha256:a66851e4533fb5b371aa0628e1fee1af05135616b86140c9d787a2ffdf4b8fdc", size = 380336 },
 ]
 
 [[package]]
-name = "bleach"
-version = "6.2.0"
+name = "black"
+version = "24.10.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "webencodings" },
+    { name = "click" },
+    { name = "mypy-extensions" },
+    { name = "packaging" },
+    { name = "pathspec" },
+    { name = "platformdirs" },
+    { name = "tomli", marker = "python_full_version < '3.11'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/76/9a/0e33f5054c54d349ea62c277191c020c2d6ef1d65ab2cb1993f91ec846d1/bleach-6.2.0.tar.gz", hash = "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f", size = 203083 }
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